Egress Modelling of Pedestrians for the Design of Contemporary Stadia 303133471X, 9783031334719

Table of contents :
Acknowledgments
Statement of Authorship
Contents
1 Introduction to Pedestrian Movement and Behaviour in Stadia
1.1 Introduction and Motivation
1.2 Literary Background
1.2.1 Previous Stadia Studies Focusing on Evacuation and Movement
1.2.2 Mobility Related Disabled Persons and Accessibility
1.2.3 Movement Speeds and Modelling
1.2.4 Existing Movement Databases
1.2.5 Relevant Codes and Standards for Evacuation of Stadia
1.3 Introduction to Study Stadium
1.4 Ethics and Related Safety Limitations
References
2 Survey of the Importance of Accessibility Features in Stadia
2.1 Introduction
2.2 Survey Methodologies
2.3 Survey Observations and Results
2.4 Analysis and Discussion
References
3 Data Collection of Movement and Behaviour of Pedestrians in Stadia
3.1 Introduction
3.2 Data Collection Methodologies for Movement and Behaviour of Pedestrians
3.3 Mobility Cases
3.4 Movement Speed Profiles of Pedestrians
3.5 Analysis and Discussion
References
4 Evacuation and Pedestrian Modelling in Stadia
4.1 Introduction
4.2 Artificial Intelligence Theorems in Pedestrian Modelling
4.3 Evacuation Model Generation and Limiting Assumptions
4.4 Evacuation Model Scenarios and Description
4.5 Evacuation Model Results
4.6 Analysis and Discussion
References
5 Strategies and Technology for Effective Evacuation Design of Stadia
5.1 Strategies for Effective Evacuation Design of Stadia
5.2 Technology for Effective Evacuation Design of Stadia
5.2.1 Collation of Movement Speed Data
5.2.2 Future of AI Technologies for Egress and Movement Modelling
5.2.3 Semi-Autonomous Technologies for Human Movement Data
5.2.4 Autonomous Technologies for Human Movement Data
References

Citation preview

Digital Innovations in Architecture, Engineering and Construction

John Gales · Kathryn Chin · Timothy Young · Elisabetta Carattin · Mei-Yee Man Oram

Egress Modelling of Pedestrians for the Design of Contemporary Stadia

Digital Innovations in Architecture, Engineering and Construction Series Editors Diogo Ribeiro , Department of Civil Engineering, Polytechnic Institute of Porto, Porto, Portugal M. Z. Naser, Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA Rudi Stouffs, Department of Architecture, National University of Singapore, Singapore, Singapore Marzia Bolpagni, Northumbria University, Newcastle-upon-Tyne, UK

The Architecture, Engineering and Construction (AEC) industry is experiencing an unprecedented transformation from conventional labor-intensive activities to automation using innovative digital technologies and processes. This new paradigm also requires systemic changes focused on social, economic and sustainability aspects. Within the scope of Industry 4.0, digital technologies are a key factor in interconnecting information between the physical built environment and the digital virtual ecosystem. The most advanced virtual ecosystems allow to simulate the built to enable a real-time data-driven decision-making. This Book Series promotes and expedites the dissemination of recent research, advances, and applications in the field of digital innovations in the AEC industry. Topics of interest include but are not limited to: Industrialization: digital fabrication, modularization, cobotics, lean. Material innovations: bio-inspired, nano and recycled materials. Reality capture: computer vision, photogrammetry, laser scanning, drones. Extended reality: augmented, virtual and mixed reality. Sustainability and circular building economy. Interoperability: building/city information modeling. Interactive and adaptive architecture. Computational design: data-driven, generative and performance-based design. Simulation and analysis: digital twins, virtual cities. Data analytics: artificial intelligence, machine/deep learning. Health and safety: mobile and wearable devices, QR codes, RFID. Big data: GIS, IoT, sensors, cloud computing. Smart transactions, cybersecurity, gamification, blockchain. Quality and project management, business models, legal prospective. Risk and disaster management.

John Gales · Kathryn Chin · Timothy Young · Elisabetta Carattin · Mei-Yee Man Oram

Egress Modelling of Pedestrians for the Design of Contemporary Stadia

John Gales Department of Civil Engineering York University Toronto, ON, Canada

Kathryn Chin Department of Civil Engineering York University Toronto, ON, Canada

Timothy Young Department of Civil Engineering York University Toronto, ON, Canada

Elisabetta Carattin Access and Inclusive Environments and Building Services Ove Arup and Partners Ltd. London, UK

Mei-Yee Man Oram Access and Inclusive Environments and Building Services Ove Arup and Partners Ltd. London, UK

ISSN 2731-7269 ISSN 2731-7277 (electronic) Digital Innovations in Architecture, Engineering and Construction ISBN 978-3-031-33471-9 ISBN 978-3-031-33472-6 (eBook) https://doi.org/10.1007/978-3-031-33472-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgments

Michael Kinsey (formerly of Arup, now of Movement Strategies), Will Wong (Arup) and Lachlan Miles (Arup) are acknowledged for their efforts in previous critical analysis of the data and modelling herein with technical contribution and mentorship of graduate trainees through regular correspondence on this project. Contributions are also acknowledged from: Danielle Aucoin who with other contributors developed and cleared study ethics and helped with initial data collection and interpretation; Danielle Alberga, Bronwyn Chorlton, Neir Mazur, Natalia Espinosa-Merlano, Julia Ferri, Lauren Folk, Kiara Gonzales, Georgette Harun, Teagan Hyndman, Kaleigh MacKay and Austin Martins-Robalino who helped review and collect movement profiles and assisted with literary review and interpretation of data; and Luming Huang who helped developed, run, analyse and troubleshooted the MassMotion modelling contributions. Hailey Todd is acknowledged for initiation of the research project. Rashid Bashir is thanked for helping with providing the initial 2018 stadium study contacts. Hannah Carton and Chloe Jeanneret are thanked for supporting the project through copy-editing and assisting in the book’s revision process. Organizations thanked for their contributions include the Arup Access and Inclusive Environments and Building Services Team, Arup UK Fire Group, Arup North Americas Group, Arup Human Behavior and Evacuation Skills Team. The Society of Fire Protection Engineering (SFPE) Foundation, CSA and MITACS are thanked for their financial support. The NSERC ALLIANCE programme is acknowledged. The Stadia managers and event organizers remain anonymous for their time and assistance in this study. York University is also thanked for providing ethics-based resources and institutional support for the data collection phase of this research project through its TD1/TD2 process.

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Acknowledgments

Statement of Authorship All persons who have meet authorship criteria in this book are listed as authors. These authors certify that they have participated sufficiently in the work to take public responsibility for this manuscript’s content, including the participation in the concept, design, analysis, writing and revision of this book. Those that do not meet the full criteria are listed in the acknowledgements above.

Contents

1 Introduction to Pedestrian Movement and Behaviour in Stadia . . . . . . 1.1 Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Literary Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Previous Stadia Studies Focusing on Evacuation and Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Mobility Related Disabled Persons and Accessibility . . . . . . . 1.2.3 Movement Speeds and Modelling . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Existing Movement Databases . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.5 Relevant Codes and Standards for Evacuation of Stadia . . . . . 1.3 Introduction to Study Stadium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Ethics and Related Safety Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 13 17 19 20 22 25 26

2 Survey of the Importance of Accessibility Features in Stadia . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Survey Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Survey Observations and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29 29 30 31 34 35

3 Data Collection of Movement and Behaviour of Pedestrians in Stadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Data Collection Methodologies for Movement and Behaviour of Pedestrians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Mobility Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Movement Speed Profiles of Pedestrians . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 5

37 37 41 45 46 49 53

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Contents

4 Evacuation and Pedestrian Modelling in Stadia . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Artificial Intelligence Theorems in Pedestrian Modelling . . . . . . . . . . 4.3 Evacuation Model Generation and Limiting Assumptions . . . . . . . . . 4.4 Evacuation Model Scenarios and Description . . . . . . . . . . . . . . . . . . . . 4.5 Evacuation Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Strategies and Technology for Effective Evacuation Design of Stadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Strategies for Effective Evacuation Design of Stadia . . . . . . . . . . . . . . 5.2 Technology for Effective Evacuation Design of Stadia . . . . . . . . . . . . 5.2.1 Collation of Movement Speed Data . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Future of AI Technologies for Egress and Movement Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Semi-Autonomous Technologies for Human Movement Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Autonomous Technologies for Human Movement Data . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 56 62 64 67 68 70 73 73 76 76 77 81 83 88

Chapter 1

Introduction to Pedestrian Movement and Behaviour in Stadia

Abstract There is a significant population of mobility related disabled persons in Canada. Recent studies have shown accessibility and mobility are a large concern in stadia egress which requires more research and practitioner attention. In 2019, Canada enacted the Accessible Canada Act to address this issue by focusing on identifying, removing, and preventing barriers that limit social, political and economic inclusion. However, the act currently requires only minimal design considerations which would be helpful to a select group of disabled persons, and for only certain types of buildings, often excluding stadia. Accessibility should offer equal quality of life for all disabled persons as well as non-disabled persons. There is a lack of research on understanding the behaviour of disabled persons in egress of stadia which designers require. Therefore, a Canadian tennis stadium will be analyzed during normal circulation and egress situations. The chapter presents a literary background for movement of disabled persons in egress and presents reviews of both Canadian and international design guidance for egress identifying areas of focus for later chapters. While the chapter focuses on the Canadian context, general conclusions may be globally applicable.

1.1 Introduction and Motivation In addition to guidance documents, such as the Green Guide, the advancement of stadia design in the last few decades has considered the emergence of novel tools and technologies that have been created by industry.1 Largely these tools are based upon accurate representation of human movement and behaviour. These tools are based upon the advancements of Artificial Intelligence (AI). Tools and technologies largely exist in the form of advanced modelling tools which aim to represent the population within the stadium. These technologies are very useful for representing crowds found within stadia and enable a designer to quickly identify areas of crowd congestion to enable new designs for improved movement in that space. Figure 1.1 1

The majority of pedestrian movement software that has been developed primarily has been done in industry settings with support from academic institutions.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Gales et al., Egress Modelling of Pedestrians for the Design of Contemporary Stadia, Digital Innovations in Architecture, Engineering and Construction, https://doi.org/10.1007/978-3-031-33472-6_1

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

illustrates the use of a pedestrian movement model to represent crossflow, bottlenecks and consequences of decision making. Contemporary stadia design requires an in-depth consideration of real human behaviour. The reliability of pedestrian movement models used for exit and evacuation design will depend on the confidence of the input movement and behavioral data of pedestrians and accurate artificial intelligence algorithms used to describe the movement and behaviour of pedestrians. For example, through default modelling practices where exits are assigned through a lowest cost procedure (see Fig. 1.1a) this may not necessarily capture all real risks and parameters present in stadia which must be considered. Figure 1.2 illustrates such an example. At the end of a sporting match, people begin leaving before the Fig. 1.1 Computational tools for stadia design of people movement a potential cross flow, b congested bottleneck, c wayfinding and decision making

(a)

(b)

(c)

1.1 Introduction and Motivation

3

Fig. 1.2 Computational tools calibrated to study exit density

end of the match. At this stadium, a video screen is present. This encourages the leaving attendees to wait under the screen to watch the remaining seconds of the match. The crowd increases in density, waiting at the exit gate. Once the match concludes, the group exits through the gate. This behaviour is a particular hazard to consider owing to the density seen. Consider if someone with a mobility aid trips in the congestion, those behind them can also fall. The knowledge of the behaviour through data collection at the stadium allows for the hazard to be replicated in a model and studied. Currently stadia design is still in need for more contemporary data with respect to surrounding pedestrian movement and behaviour. This is true for most infrastructure where mixed demographics and high capacities may be expected. There is a significant population of mobility related disabled persons in Canada. Recent studies have shown accessibility and mobility are a large concern in stadia egress which requires more research and practitioner attention. There is a lack of research, which designers require, to understand the movement and behaviour of mobility related disabilities in persons in the egress of stadia. Herein, a full-scale observational study of a real stadium is observed and analyzed to reinforce conclusions and movement trends which will be reported. Individual pedestrian movement and behaviour is studied with specific focus on accessibility, inclusion and disabled persons. This book recognizes that stadia design is at a revolutionary stage of advancement. Various technological gains in data collection methodologies will be described where the authors work towards the collection of big data which can be used for future refinement of modelling technologies and Artificial Intelligence (AI) routines. Automated analysis technologies are also described where they may be more advantageous to replacing manual or semi-automatic methods. While the authors will focus on Canadian infrastructure primarily, the results will be useful within a global context and discussion on Stadia Design. The first phase of the study is a survey regarding the public and staff’s knowledge on accessibility features in the stadium considered, as well how they believe the

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

stadium design could be improved to be more accessible. The survey study is done to recognize factors such as the architecture of the stadium, that affect the mobility related disabled population from attending or not attending. This survey acts as a baseline for how the stadium is currently functioning. Specifically, the survey evaluates how accessible the stadium design is, pointing to immediate improvements which would foster an increase in mobility related disabled persons attending the stadium events. The second phase of the study builds upon these findings with an observational study which focuses on collecting population and movement data on current demographics using the stadium. Movement profiles for mobility related disabled and non-disabled persons will organize the parameters of respective microsimulation agents in terms of mean walking speeds. This includes the following occupant and building characteristics of interest: • Horizontal movement of mobility related disabled persons (for example those using wheel-chairs, canes etc.); • Persons with other reduced mobility conditions (for example those carrying large luggage, or those travelling with family); • Persons with various body shapes and sizes which may influence mobility (obesity); • Intoxicated persons (ex. herein ‘any’ alcohol consumption is inclusive of this category); and • Persons transversing stairs. Collecting this movement data allows for it to be placed in pedestrian evacuation and movement models for circulation and thus allows designers to consider their needs in the design of future structures to enhance safety. The datasets derived enabled the authors to illustrate the effect of egress model parameters to reflect in-situ environment conditions. The concluding datasets are then used in the third research phase where simulations are created. These allow engineered designs to be simulated beyond the existing level of an assumed homogeneous population by incorporating non-homogenous populations of individuals in stadium crowds which would be expected in future use. These models include using the current default parameters (demographics and movement speeds), manually adjusting parameters with and without mobility requirements, and manually inputting parameters for a forecasted population with a higher population of disabled persons. The final simulation addresses how changes in mobility related disabled persons demographics may institute specific design requirements and future research areas. The last stage of study considers how the data collected can be used for effective design while giving attention to renewed technologies that may help with the collection of future data that may enable future theory to develop to improved stadia design for people movement. This book aims to improve the environment to prevent the act of disabling persons, and to further promote accessibility and safety. The book was divided into several chapters to meet the aforementioned objectives. This first chapter has introduced various aspects of the field of study to introduce the reader to the case study and relevant literature. The research is established in

1.2 Literary Background

5

its use and novelty. This chapter will introduce the reader to the subject matter and provide the background information necessary for the reader to understand the terminologies and theory being explored in the book. It will focus on behavioral aspects of people in stadia, a brief literary review to the subject. It will then explore the current theory behind modelling used to describe people movement in stadia and the artificial intelligence behind this theory. Lastly, it will introduce the reader to the original research being used in the book and the ethics clearance involved to undertake the study. Chapter 2, Survey of the Importance of Accessibility Features in Stadia, will explore the demographic breakdown which is seen in contemporary stadia. A survey will be introduced that was used to undertake a quantification of accessibility features at the stadium. This portion of the study aims to demonstrate how improvements to the design of the stadium and its grounds can improve inclusivity and safety. As well, it aims to show that working with the population directly affected by the design of the environment can help to create universal spaces. Chapter 3, Data collection of Movement and Behaviour of Pedestrians in Stadia, introduces the reader to the data collection process with emphasis on current and emerging technologies used to capture movement trajectories from imagery. The resulting movement profiles for all demographics will be presented which include disabled persons and movement effects from alcohol. Chapter 4, Evacuation and Pedestrian Modelling in Stadia, will explain the artificial intelligence theorems used to describe people movement in pedestrian modelling. The construction of a stadium model will be explained with appropriate limitations described. A suite of scenarios will be considered to explore future demographic trends in stadium and their effect on movement and evacuation. Chapter 5, Strategies for Effective Evacuation of Stadia, the chapter will present emerging trends in pedestrian movement and development of requisite software to describe their behaviour. Future research needs will be described, and new technologies being used to track movement and improve data collection will be presented and reviewed.

1.2 Literary Background 1.2.1 Previous Stadia Studies Focusing on Evacuation and Movement In 2018, the Society of Fire Protection Engineers launched a multi-year study to investigate and produce new movement speed profiles that could be utilized in various pedestrian movement models (see Gales 2020). This was project was led by York University researchers and collaborators and leveraged through a MITACS grant with the SFPE St. Laurent Chapter in Canada. Industrial collaborators included the design consultancy firm Arup. This research endeavor focused on several unique

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infrastructure types. This included care homes for the aged (Folk et al. 2020), cultural centers (Gales et al. 2022), airports and commuter stations (Gatien et al. 2022; Young and Gales 2022), and focus herein in this book, stadia (Chin et al. 2022a, b; Young et al. 2021). The latter stadia will be reviewed prior to discussion into the theme of accessibility and movement as this was not considered in the previous studies as will be described. Previously Chin et al. (2022a, b) investigated the current demographic breakdown of stadia users by age in Canadian football stadia and illustrated profound differences in movement based upon the age of the population. Chin also verified that fundamental speed reductions associated to levels of service drops as would currently be the practice to assume, at least for small to medium sized density. This study also found that high levels of congestion in stadia predominately involved older persons (generally noted as 65+) in the crowd. Figure 1.3 illustrates the movement speed data collected and the corresponding density through a stadium corridor with 511 persons. Level of service is also noted which corresponds to specific population densities where movement is expected to be impacted. The speed data allows for a fundamental diagram to be constructed of speed reduction with density. In a companion study, motivation stimulus for egress was studied by the authors (see Young et al. 2021). In that study, a normal egress event was filmed and analyzed by the authors, a high motivation Egress event was filmed and analyzed by the authors, and archival video recordings of a fire egress event was analyzed. All events occurred in the mid to late afternoon in the day. The same filming methodologies were utilized for the normal and high motivation with the same cameras seen in Chin et al. (2022a, b). The Fire Scenario used several cell phone recordings from spectators including a ‘birds-eye’ view which was taken from an adjoining apartment building. Only the 2.5 Level of Service A

2.25

B

C Child

Speed (m/s)

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Young Adult

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Adult

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1.25 1 0.75 0.5 0.25 0 0

0.1

0.2

0.3

0.4

0.5

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Local Density (persons/m2)

Fig. 1.3 Movement speed and local density seen in a Canadian football stadium (adapted from Chin et al. 2022a, b)

1.2 Literary Background

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specific information is reviewed herein to illustrate the effect that various stimuli can have in terms of motivating egress and influencing the behaviour and movement of people during egress. The reader is encouraged to review the full study which is available in open access via the author’s publication archive. That study (Young et al. 2021) did not consider holistically mobility related disabilities in these populations and there is still a requisite need of study. Table 1.1 lists the primary studies and the evacuation stimuli considered in Young et al. (2021). All film from studies normal and high motivation were analyzed individually by multiple members of the authors’ research team and results were later compared to reduce subjectivity of visual observations. Flow counts were also performed at the exits by counting the number of patrons passing through at approximately five second intervals and were added into the timeline after the flows were determined. This was the same procedure used to count exit use proportions for wayfinding behaviour. In the normal and high motivation evacuation studies, two carefully selected vantage points in the stadium were recorded, as displayed in Fig. 1.4. Dimensions for gates and stadium were taken from a CAD file provided by the stadium. Each gate is 2.54 m wide at its entrance, which is the narrowest point, and the walkways leading to these exits are each 2.89 m wide. Artificially induced events have ethical issues and a drill could not be relied upon to study effects of egress. Instead, the authors focused on adverse weather, such as a sudden storm, which itself has limitations as people will not be moving with same urgency, people who need assistance may not be waiting, or people may not be using the same routes. It also implies that people will need more space in their evacuation due to the use of umbrellas. A rain event (storm) would induce an evacuation of the tennis stadium as the play of the game would be suspended. It should be noted that not all sports will follow this evacuation procedure; in Canadian football for example, play will not stop when rain occurs and is continuous. It should also be noted that it would not be possible to control the occupancy of the stadium the moment of downpour. Therefore, as indicated in the above table the number of spectators is of low capacity. Figure 1.5 illustrates imagery taken from the rainfall event. The fire egress study reviews the event of a localized fire at a Canadian Football stadium (the same stadium as noted in Chin et al. 2022a, b). Recorded footage comes from seven short films shared by spectators to the authors. The focus of the case study herein was on the localized stand area. Clear footage for the adjacent stands was not publicly available. Figure 1.6 describes the percent population egressed with time and the flow of persons per minute per exit width with time for the standard and high-motivation Table 1.1 Egress scenarios collected and studied by Young et al. (2021) Stadium type

Filming date

Attendance who egressed

Egress type

Tennis

2019

12,000

Normal (post-game)

Tennis

2019

2000

High-motivation (rain)

Football

2018

128 (one stand)

Emergency (fire)

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

Fig. 1.4 Filming locations for normal and high motivation egress

egresses, respectively taken at the same two gates ‘B’ and ‘C’. The normal postgame egress was very long and distributed (spread out). The first major spectator egress starts at time 0:00 with the completion of the game. Egress slows at around 1:40. At 1:40, an interview with the winner begins and is broadcasted in the stand jumbo screen. During the interview, egress slows down. The interview ends at 3:48 where those in attendance begin applause, followed by the next major spectator egress at 3:57–6:50. During this time, the greatest flow is recorded as 89 ped/min/ width. From 6:50 to the end, the remaining 20% slowly dissipate (see Fig. 1.7). In direct comparison, the egress behaviour observed for the high motivation rain evacuation displayed a very different trend; the evacuation was faster and steadier (see Fig. 1.8 adjusted to the same scale as 1.7). The pre-movement time was overall short (about 10 s), with some spectators (about 5%) beginning their egress before the game suspension announcement. The rain quickly intensified, potentially subverting the optimism for spectators that the rain would be minor and leading to a greater incentive for people to seek shelter inside the back of the concourse which is not exposed to weather. The persistence of the rain may have acted as a constant evacuation cue to which the spectators were exposed. The greater flow observed is what led to queuing and congestion that was unseen in the regular egress (see next section). It is important to remark that peak densities (120 ped/min/m high motivation and 90 ped/min/m normal) seemed to be correlated to the announcer calling the cessation of activities of the match regardless of the stimuli. However, the densities were higher with the stimuli despite the lower number of spectators in the stadium. The total recorded egress for the fire case study at the football stadium was analyzed as over 2 min 55 s for the fire evacuation at this stadium. Approximately 120 spectators evacuated the local stands from the time that smoke was visible. It

1.2 Literary Background

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Fig. 1.5 High motivation evacuation showing older person with a ‘cane’

is not clear in the footage if an alarm to evacuate was sounded as the play of the match continued. It is certain that visible cues for evacuation could be seen by attendees. The origin (00:00 time) is taken as the earliest available footage the authors obtained, when the fire had already begun, and dark smoke was emanating from the stands. Despite visible smoke and flames, non-involved fans did not begin to egress until the small explosion was observed, over 35 s after the start of the video footage, and more than 30 s after fire was visible. The smoke appears as though it did not act as a sole cue to evacuate. The explosion correlates to when some of the pedestrians were seen to move (albeit not to fully evacuate at that point). This may be an example of normalcy behaviour, as the spectators could have thought that the

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1 Introduction to Pedestrian Movement and Behaviour in Stadia 100%

Fig. 1.6 Normal versus high motivation % population egressed

90%

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situation was somewhat low-risk and normal. There is additional need to study the effect of multiple layers of senses (smell of smoke, sounds of explosion, feeling of heat) correlating to the initiation of evacuation by an attendee. For example, even after the explosion, most spectators only shifted over a few seats, and it took a few seconds for another group of spectators to start their egress. During the stadium fire event, 16% of spectators were observed filming the incident on their cell phones, sometimes blocking egress routes or even getting closer to the incident. The continued filming by attendees may also be an example of optimism, as those who chose to film likely assumed that they could do so somewhat safely, and that there was no danger present in doing so. This filming behaviour could also be attributed to bandwagon behaviour, as one person filming influences another and so on. Additionally, this could be an example of attentional behaviour as spectators are focused on the event as it unfolds, missing potential cues as the situation evolves. An illusion of control can be observed after the banner catches fire, as some members attempt actions to keep their fire under control, despite lacking a fire extinguisher. Bandwagon and authority behaviors were also seen as most masked individuals stayed together in the stands, with some following one flag-bearer when he made his way to the exit. The authors though suggest that the behaviour being seen is more akin to a social identity being developed among the fire setters (see Templeton et al. 2015). There is a need to further study in stadium fires the filming behaviour relating to social responsibility for gathering evidence of for police or stadium management. The sharing of the film to the public is an example of this. The filming behavior seen in this stadium study can be supported by very similar behaviors presented at two notable stadium fire case studies—the recent 2019 fire at Nissan Stadium, and the historic fire at Bradford City Stadium in 1985—discussed further in Young et al. (2021). Behavioral similarities can be drawn between the three egress stimuli studies. All three took place in a Canadian sporting stadium and resulted in an effectively complete egress of spectators. Of the two non-standard egress events, both occurred under high-motivation stimuli (rain and fire). All scenarios included some degree of influence of authority to prompt or influence evacuation, whether from announcers or stadium staff themselves. However, significant differences were also observed with regards to total egress times, pre-movement, behaviour, and observed congestion. Despite the significantly larger population and size of the rain event, total egress times were less than the observed time for all of the emergency fire events considered. The authors believe that this is heavily influenced by the perception of threat communicated by the authority figure—for example, in all fire case studies, the size of the fire correlated to the urgency of the staff to evacuate spectators. The stimulus of the rain also affects all people in the stadium which may have caused increased congestion of all egress routes whereas the fire event was a highly localized event so levels of high motivation were differently spread between the two events. Higher pre-movement times in fire scenarios contributed to the longer egress times, though where staff was unsuccessful in having members within the stand leave which possibly leads to the role of social identities and group formations within the stands (i.e., those filming or those causing vandalism for small examples).

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

In the Canadian Football Stadium Fire event, for example, some members of the population were actively participating in the events leading up to the fire. This resulted in a longer egress time for these attendees as they had to be directed by staff members to evacuate the stands. These actions by the staff appeared delayed requiring mobilization. A fire in the stands was not an expected occurrence during a game (fireworks were not permitted on the grounds for example). In contrast to this, the suspension of play due to rain in a tennis game is an expected occurrence as rain was forecasted hence attendees also having umbrellas. Therefore, the high motivation (rain) event was potentially expected by both spectators and staff. Staff response in this case was also much quicker, with play suspended only a few seconds after the initial start of rainfall, and some spectators standing to leave even before the suspension of play was announced. The egress during the high motivation rain event was implied through standard procedure and an announcement by the Chair Umpire. The Chair Umpire’s announcement to suspend play has an effect of directing the audience how to act. In the case of the high motivation rain event, this appears to have induced the evacuation process for most of the audience. In the case of the normal egress, it delayed it and contributed to congestion and cross flow. Similarly, during the Bradford fire, police officers directed evacuees to enter the pitch, influencing the route evacuees took—but this was only followed upon when the game was stopped. These are examples of Authority bias (Tversky 1974). Conversely, the lack of instructions from a figure of authority can have a delaying effect, as observed in the standard egress which came with no explicit instructions on when to leave, or the Nissan and Canadian football stadium fires where authority figures did not direct egress until after attempting to put out the fire. For example, the reaction of authority was much faster than that observed with the Canadian football which is likely due to the size of fire observed and associated threat. The major behaviors of the emergency event, namely attentional, optimism, and bandwagon behaviors were observed in all scenarios (Kinsey et al. 2019). However, these behaviors were not as widespread, and the effects manifest in slightly different ways. Similar to the fire event, attention played a role for some spectators who continued to watch the tennis game even as the rain started to fall. However, this was quickly subverted by the announcement of suspension of play. With the item holding the attention removed by an authority figure, the decision for many could have switched from stay to evacuate. However, this does not mean all spectators evacuated. Optimism behaviour also likely played a role for some audience members who chose to stay in their seats with umbrellas and try to wait out the rain. However, as the majority of the audience chose to evacuate, bandwagon behaviour may have encouraged many of those who initially tried to wait out the rain to evacuate as well. In the standard post-game egress, potential attentional and bandwagon behaviors were also observed, as multiple rounds of applause and events caused leaving spectators to pause and, in some cases, return to observe post-game activities in the stadium. This had a positive effect on egress in this scenario, as it reduced the flow of people through the exits and preventing queuing from occurring. Pre-movement behaviors also substantially differed between the events. Typically, pre-movement is defined within the safe egress time model. It initiates before the

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actual movement of people to egress. For example, at 15 s, 97.5% of the population was in movement towards an exit in the rain event compared to 8% of the population in the regular end game egress. It would not be until over 4 min for an equivalent majority of the population to be in movement in the regular end game egress. During the actual egress of the rain event, several spectators were observed running to the exit. This is a high contrast to the Canadian Football Stadium and other fire events, where spectators were only observed walking to the exits. It is important to also note the cultural and demographic differences present in each event, as the audience of a football match likely has a different demographic distribution compared to a tennis match. The audience members involved in the fire events appeared to be young adults and adults, whereas both events at the tennis stadium consisted of a much more diverse population, with families and seniors also observed in larger numbers. In terms of movement, observations from the fire event show that their pre-movement times can be longer than expected, especially if an individual is a member of a group closely involved in the situation. Cultural implications can have an additional effect through anchoring behaviour. During standard post-game egresses, cultural expectations of post-game events can distribute the pedestrian demand over a longer period of time to prevent crowding. Conversely, this can have negative effects in emergencies, with the football culture of not going onto the pitch causing some delay in getting evacuees to evacuate onto the pitch during the Bradford fire in 1985. The authors also remark that neither of these egress situations noted above for stadia illustrate significant populations which would visibly require assistance in evacuation. For example, those using equipment (canes, white stick, wheelchairs, etc.). As this may change in the future, there is benefit to study the consequence on these population projections with validated and verified modelling. With advanced movement models which can fully describe contemporary behavioral models within its artificial intelligence algorithms, a more pronounced need exists for understanding of people’s accessibility needs, and the barriers that may affect people with different identities and circumstances (e.g. different levels of mobility, age, etc.) to alleviate any act of disabling a person and thus offer equal quality of experience in the design of stadia. A lack of knowledge exists for stadia concerning these demographics, specific accessibility and inclusive design solutions, available movement, and behavioral data.

1.2.2 Mobility Related Disabled Persons and Accessibility Disability is a significant area to be considered in the design of stadia and any infrastructure type. A 2017 Canadian Survey on Disability Reports identified that 22.3% of Canadians over 15 years of age are considered disabled (Morris et al. 2018). This classifies disabled as specifically resulting from injury, illness, or congenital condition. Disability falling within the psychological or physiological variety. This includes, but is not limited to, disability relating to sensory skills, speech, communication,

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

Fig. 1.9 Example of aided movement of a infant in exiting a stadium (authors photo)

mental health, mobility, and cognitive health. The associated disability however is not defined solely by their medical condition, but rather the attitudes and structures of society that act as barriers in ultimately disabling persons, as outlined by the social model of disability (Oliver 2012). Another Canadian survey showed that 32% of participants believed the government should direct their primary attention regarding accessibility to the built environment (Employment and Social Development Canada 2017). This was the secondhighest response which indicates that there are many items in the form of physical barriers within the built environment that can prevent or limit accessibility.2 Mobility is one of the biggest concerns related to disability as reported by 9.6% of Canadians over 15 years of age (Morris et al. 2018). In addition, an even greater proportion of the population experiences reduced mobility under common circumstances such as transporting oversized luggage, leading young children, assisting another person, obesity or intoxication. To varying degrees, accessibility affects all people as it involves the quality of reaching, entering and using space (see Fig. 1.9). To recognize and protect disabled people as well as further promote equal rights, Canada created the Accessible Canada Act. This aims to enable participation for all persons, by focusing on identifying, removing and preventing barriers that limit social, political and economic inclusion (Government of Canada 2019).3 However, the act states that new or refurbished public buildings should provide access for all and that existing buildings should make “reasonable” provisions within the context of what is currently existing in the infrastructure. This wording is suggestive in that only minimal building design and management considerations are required, which at best eases access and use for only select groups of disabled persons. These minimum requirements imposed by this wording can therefore lack in compliance 2 3

Accessibility refers to the quality of which the environment can be entered and used by all. There is no explicit mention of evacuation currently detailed in the current ‘Act’.

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and enforcement. The Act also does not acknowledge the importance of offering equal opportunity and quality of life by not fully considering the diversity of human abilities and their limitations. As it stands, the Act is not fully accommodating mobility related disabled persons which herein we define as individuals using mobility devices (example being wheelchairs, canes etc.). The Act gives little to no attention to and aid for the multiplicity of disabilities, especially those that fall under the category of invisible disabilities.4 Another issue with the Act is that its overall application is limited to federally regulated entities. Therefore, a significant portion of community services, including stadia, are unaccounted. Currently codes, standards and regulations in Canada are relatively immature in their application. This creates an absence of accessible design5 in stadia and limits the ability of disabled persons to attend events and work at these locations. This leads to a lack of data to help understand their behaviour in these situations. Evidently, designers may be deterred from attempting accessible design in stadia because they are not required to under the Accessible Canada Act, and because they do not understand the behaviors and characteristics of disabled persons. Accessibility is a matter of civil rights, equity, public safety, and business prosperity, with the collective goal to provide an inclusive universal design.6 This means creating safe environments that offer equitable access and use by the greatest possible extent by all people regardless of their age, size, ability or disability. The design must account for all measures of human abilities to offer a safe environment while offering an equal quality of life for disabled and non-disabled persons. Both public and private establishments can otherwise deter individuals from using their facilities if accessibility is not adequately available. Currently within Canada, various standard bodies (ex. CSA) are working to refine standards for possible adoption within building codes. While these discussions and documentation are underway there is growing momentum to see this design more codified in this jurisdiction.7 The specific study of mobility related disabled persons is an emerging field, with a primary focus on evacuation as noted by Haghani (2020). However, in Haghani’s 4

An invisible disability, also described as hidden or non-apparent, more specifically defines the group of disabilities that are associated with symptoms that are less obvious and not immediately apparent on the outside. 5 Accessible design is acknowledged as an environment that meets the minimum requirements to achieve usability by recognizing functional limitations and varying capabilities of its occupants. To fully offer equal independence, choice and control to all users, the environment requires an advanced design that welcomes the diversity of human abilities and creates a multitude of accommodations so as to not disconnect from any individual need. 6 The terms universal and inclusive are sometimes interchanged in design settings, such that they hold essentially the same principles and objectives, however, differ in their areas of application. Inclusive design is a much broader definition in which various solutions would be implemented to help various groups of people. Universal design focuses on creating an accessible environment for the most amount of people rather than specific groups. For consistency, the authors thus chose to use universal design henceforth, as it is believed to be the more suitable and appropriate terminology for the applications this study. 7 As part of the CSA student scholarship program, the initial movement profiles herein were sponsored to develop movement data which could be utilized within standards discussion (refer to acknowledgements).

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

review this consisted of studies that primarily focused on qualitative interviews, in contrast to the lab and field observation studies of general populations which measured flow rates, movement speeds, and pedestrian trajectories. A few studies of movement speeds focusing on disabled persons do exist, including the works of Sørensen who carried out evacuation studies of disabled populations in familiar buildings and transportation infrastructure. Their studies highlight the importance of considering these diverse populations and their evacuation needs and behaviour (Sørensen and Dederichs 2013). Research into blind or partially sighted persons in familiar environments revealed comparable horizontal walking speeds impacted by the degree of vision, and often relied on walls for tactile orientation and navigation (Sørensen 2013). The older persons population (65+) was notable in a modelling study of a train evacuation, demonstrating that the egress time was not conservative in the modelling software that they used, and that significant behavioral differences existed when considering heterogenous groups. This was due to actions in the heterogenous group such as assisting other populations, and not overtaking during egress (Sørensen and Dederichs 2014). For circulation, Transport for London published their own movement speeds for different types of disabilities (Transport for London 2012), which is incorporated into Pedestrian Modelling software (Oasys 2019). For evacuation, a classification framework for disabilities and their impact on evacuation performance was recently developed (Bukvic et al. 2021). This review looks at the behavioral level and considered activities that could be difficult to perform considering each functional limitation, including (but not limited to) sensory, physical, and cognitive differences. Of interest is a section examining movement on horizontal, inclined, and corner walkways, which looked at movement speeds from three studies in an experimental setting. However, field accessibility studies specifically looking at stadia could not be found. This is important, as Bosina and Weidmann found that the movement characteristics of pedestrians may vary depending on several additional factors, including country and type of built environment (Bosina and Weidmann 2017). This book by the authors herein will necessitate the analysis of both disabled persons with mobility requirements, and other reduced mobility conditions, to evaluate a wide spectrum of disability cases. While the authors are aware disability is a much wider term including those with invisible, learning, cognition, sensory disabilities as well as non-disability related factors such as culture and gender (Employment and Social Development Canada 2017), these were outside the scope of the study herein. The data from this study is to be used as a baseline for how future design, planning and legislation should change to accommodate a more diverse population particularly within stadia design. There is a lack of research that designers require to comprehend the impact of influencing factors on a persons’ response to various scenarios, and the subsequent ability to adapt to such stimuli. Crowd modelling is used to ensure the safe design of structures; modelling stadia is the third most use of pedestrian evacuation models (Lovreglio et al. 2020). Having a detailed database on human behaviour and factors will allow for increasingly refined and highly sophisticated crowd modelling to represent the true diversity of crowds by incorporating the vagaries in individual movement

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patterns subjected to behavioral and environmental factors (Hurley 2016). However, many studies do not consider movement profiles of disabled persons (Ronchi et al. 2019). In the case of disabled persons, it is their mobility and the accessibility offered by the facility that will influence the person’s ability to move throughout the structure. Walking conditions can be defined quantitatively in an engineering context as speed, density and flow. The authors recognize these are all abstract concepts. They are subject to be impacted by a variety of factors. Individually for example as the person’s weight, height and familiarity with the structure or within groups such as for example friends or family members. This idea is described as reification in which these abstract concepts are treated as tangible objects (Carattin and Brannigan 2014). However, creating specific movement profiles for disabled persons aims to reduce some of the uncertainty by limiting some of the impactful factors. For example, speed is defined as the distance covered per unit time. However, attainable speed, congestion, barriers along the route, etc. experienced by the pedestrian are subjected to the following characteristics: occupant characteristics such as crowd demographics, and respective movement capabilities; building characteristics such as the architecture, location, activity type, building purpose, and features of stairways, corridors, etc.; and motivational characteristics that differ depending on circumstances of normal pedestrian circulation, ingress/egress, high-motivation situations (for example, rainfall), and emergencies. Architectural features are known to affect egress times as factors such as the merging angle impacts movement speeds These are the various factors of consideration that comprise the input parameters for model configuration (Shiwakoti et al. 2015). Movement of those who are overweight are also under-researched despite a separate study in 2018 showing that 63.1% of Canadians aged 18 and older as overweight or living with obesity (Statistics Canada 2019). In addition, intoxication from alcohol use is common during stadium events, however, there is little knowledge regarding the impact of this intoxication on movement speeds in stadia. This caused interest in determining movement profiles for those who are overweight or intoxicated to examine the relationship of weight and alcohol to speed.

1.2.3 Movement Speeds and Modelling One element of comprehending crowd behavior involves the evaluation of the spectrum of movement abilities represented by a population. In reviewing past studies on crowd movement and dynamics, Thompson et al. (1997) introduced a generalization on crowd parameters and a lack of analysis of individualistic behaviors in the crowd. They introduced a new parameter: the inter-person distance or contact buffer. This parameter is defined by the amount of space a person would leave between themselves and the person in front of them to avoid collision should any sudden change in movement occur. Hoskins reviewed the flow dynamics in past studies to develop the theory that stair movement depends greatly on individualistic behaviors, in which the

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crowd is limited to the pace of the person in front of them. Therefore, in calculating speed and flow on stairways, the slowest individuals should be prioritized in the evacuation analysis. Larsson et al. examined the varying crowd compositions at a variety of stadium events (concerts and sporting events). The study found that the type of event governed the demographic distribution, and therefore the egress at the stadium (Larsson et al. 2020). Under all circumstances, assuming homogenous crowd density is a serious limitation in existing models and equations that do not account for the variance in movement capabilities. Assuming homogenous density for the crowd is not an accurate nor conservative approach to crowd modelling (Hoskins 2011). Although there have been several studies investigating crowd and individual movement adapted for use as movement profiles in crowd modelling (Hurley 2016), some used as defaults may be dated and may no longer be representative of the current or future population due to the significant changes that demographics have undergone. For example, Fruin created a movement profile for all pedestrians through the study of a New York subway station (Fruin 1971a). Another widely referenced study was completed by Ando et al. in which four demographics were considered and movement profiles for each was created (Ando et al. 1988). However, these studies are limited because they were created in the 1970s and 1980s. These profiles are now outdated since demographics change over time, and because they did not consider mobility related disabled persons, only the general public. Authors of these earlier datasets have requested that their data not be included in the SFPE handbook (mentioned therein), potentially to avoid being used in engineering calculations (Oasys 2019). Therefore, new movement profiles are needed, which may now incorporate extensive studies on the occupant characteristics, involving crowd demographics and anthropometric profiles such as age, sex, grouping behaviors, and physical abilities. Recent studies have begun focusing on the crowd and individual movement. Larusdottir and Dederichs, and Najmanoca and Ronchi for example, have exclusively studied the movement characteristics of children to outline the difference in behaviors this age group presents in comparison to previous studies analyzing mostly adults (Larusdottir and Dederischs 2011; Najmanová and Ronchi 2017). Rahouti et al. completed fire evacuation drills at a healthcare facility in New Zealand. A small portion of the population (10%) were disabled persons and found that staff members were an important part in guiding and initiating evacuation for patients. In addition, they found that disabled persons were able to move about half the speed of non-disabled persons (Rahouti et al. 2020). Another study stressed the importance of modelling disabled persons and provided premovement and horizontal movement data for those with physical, cognitive and age-related disabilities (Geoerg et al. 2019). While these studies are more recent, they do not specifically look at disabled persons, or are not directly focused on stadium movement. Other studies have looked at the qualitative aspects, such as spacing, passing, group behavior, and handrail usage. Staircases are of a particular interest, as they present additional constraints that require specific attention in their design, and additional effort by occupants in the attempt to traverse the stair components. In 1971, Fruin investigated how the physical dimension of human body impact the practicality of moving in stairways and how the perception of personal space impacts movement

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characteristics (Fruin 1971b). Pauls conducted numerous studies in the 1980’s on evacuation, however, his most notable work has to do with stair safety and his introduction of the ‘Effective Width Method’ (Pauls 1984). Graat et al. (1999) completed a study regarding pedestrian egress on stairs in a stadium, and how their egress times can vary based on their motivation (Graat et al. 1999). More recent studies have been completed regarding stair movement. For example, Shi et al. conducted notable studies to categorize movement patterns based on dimensions, incline and stair configuration (Shi et al. 2009). Bergqvist extended these studies to analyze the true walking area of a stair, and the impact of different spiral stairway dimensions on evacuation (Bergqvist 2015). Kuligowski (2014) published studies on stairway movement in a high-rise building to provide more detailed data, since it was believed that the existing studies lacked the data points required for modelling software. Additionally, the performance of disabled persons and those in need of assistance during an evacuation was evaluated (Kuligowski et al. 2014). Sano et al. also completed a study on how to model evacuation with stairs for multi-storey buildings. Specific focus was placed on the stair merging ratio and how varying ratios can impact egress times (Sano et al. 2018). None of these studies, however, have examined movement behaviors on stair configurations similar to this study. The existing studies focus primarily on standard stairwells that consist of landings that provide a tread space of up to three treads, after about ten to twenty stairs, whereas the study discussed herein consists of up to 54 consecutive steps, with no use of landings. The study herein focuses on a normal circulation and non-emergency egress situation, the results cannot be applied to an emergency evacuation without additional study. Grimard and Sinapi completed a study about the emergency evacuation of disabled persons and the challenges they face in high-rise buildings in the United States. High rises are rarely designed for the evacuation of disabled persons who have difficulty moving on stairs. Often, they wait in a refuge area or require assistance travelling down the stairs (Grimard and Sinapi 2021). Using disability movement profiles from Boyce et al. (1999), 14 egress simulations were run with varying portions of populations evacuating using the stairs and elevators. Results showed that stair evacuation was slowed by disabled persons because faster individuals would not pass slower individuals (Grimard and Sinapi 2021). There is limited research on when both elevators and stairs are used with disabled people and the area has been studied largely computationally (Chen et al. 2020).

1.2.4 Existing Movement Databases To implement viable options that offer safe and equal accessibility for all persons, detailed and comprehensive reference data must be available to enable the development of theories, regulations and standards. In recognition of this, the SFPE handbook has created a collection of studies on movement speeds and tabulated the results in a uniform fashion. These tables include unassisted and assisted unimpeded speeds

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1 Introduction to Pedestrian Movement and Behaviour in Stadia

on the horizontal, on ramps, and on stairs (ascent and descent). Each dataset is summarized with a predefined set of parameters: descriptive statistics (mean, standard deviation, range), the source of the data, observational conditions (location, nature of the study, spatial configuration, participants and the variables), the sample (collection method and size), the results (density, speed, relationship between speed and density), and additional information such as stair configuration (direction of movement, slope distance) if applicable (Hurley 2016). These tables, however, are currently limited to nine trial summaries and thus lack the availability of varying environments, crowds and exposures; none of which are comparable to the parameters of this study. None of the listed trials in the study by Hurley (2016) take place in North America, nor evaluate sports and entertainment complexes, nor consist of a sample population greater than 100 persons. Evidently, the current handbook, as of the time of writing this book, requires extensive attention to expand and diversify the data. This is currently an issue because representing the movement of people properly has critical importance for the design of buildings and infrastructure—particularly in the case of emergency or evacuations with urgency, but also, for normal day-to-day circulation.

1.2.5 Relevant Codes and Standards for Evacuation of Stadia Contemporary stadia design follows academic interest created with the groundbreaking stadia study SCICON (1972) in the United Kingdom. SCICON led to the creation of a Guide to Safety at Sports Grounds (1976), better known later as the contemporary Green Guide still in use at the time of writing and in new edition (2022). The motivation for the SCICON study surrounded repeated disasters at stadia where life loss incurred. More specifically motivated by the IBROX incident in 1971. After the 1970s, there was momentum being created from disasters to better understand human behaviour fundamentals in various infrastructure types (see Gales et al. 2022). Subsequently the original guidance formulated in the SCICON report is mostly omissive of underlying people behaviour and focuses more on quantitative markers that generalize movement behaviour in the form of quantitative minute rules to describe and quantify people’s behaviour. There is pertinence to describe the methodology utilized within the SCICON report because these minute rules are often referred to in contemporary design documents, more specifically what is called the eight-minute rule in industry. They are cited with omission of the needs of all occupants in egress and general movement in stadia as described below. Their definitions are often lost with the obscurity of the reference material for which they are defined within. Examination of original documentation, as discussed below, will reveal that the methodology for data collection in SCICON is largely qualitative from researcher observation and would not hold to the scientific vigor of modern and contemporary human behaviour studies.

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Twenty-three field observations were performed at 15 stadiums in England and Scotland. The researchers note that ‘some’ quantitative observation was performed from time lapsed photography though this was very limited and hand counting was largely performed for data collection. The minute rule that was ultimately established to govern movement to exits was subjectively defined by these authors as when anxious states in movements could be seen. This was correlated to peak densities that the authors showed that were occurring at roughly 7 min in the stadia considered by the authors. It is important to regard that this is not the peak maximum density that could occur, just the peak density that did occur in these stadia. Those authors’ state: Having decided that crowding should only take place in the enclosures, observations and measurements were made to find out to what extent crowds could be retained in these areas without excessive pressures building up. In the early stages observers concentrated on studying the relationship between density and pressure. It was postulated that if the density at which pressures become dangerous could be established then the problem could be simplified to that of controlling crowd densities. However it was found to be impossible to measure pressures within the crowd and, although we could measure crowd densities from film, we could not model the way density built up around exists.

They further remark: During peak flows exceeding seven minutes spectators were observed to be under pressure.

The Green Guide would later adopt this guidance specifying the minute rule as the occurrence where it takes more than seven minutes to vacate an area of spectator accommodation (stands) where the crowds would then become turbulent in flow and the individuals would lose control over their own movement and the rate of flow reduced (as defined as early as 1976). In general, the stadia considered were standing. That is, they were non-seated stadia. This would be omissive of most populations with mobility related disabilities for whom it might be difficult standing for long durations. This book is not to debunk or challenge the minute rule in literature, only to illustrate that it was non-inclusive of demographics and their movement abilities as were most studies at the time. Hence, the need of new studies and data collection which would follow. In general, it was not until the later 1970s and early 1980s that Jake Pauls’ National Research Council of Canada studies began to recognize the importance of various demographics and their capabilities in movement for the design of stadia. In Canada, reliance is given to minute rule procedures found within the NFPA standards which are slightly more refined. It defines that large stadium are typically designed in accordance with the “smoke protected seating” where attendees can clear the seating area and reach an egress concourse in a certain amount of time. Smoke protected seating requires provision for a smoke control system or natural ventilation designed to maintain the smoke level not less than a person’s head height (6ft) above the floor of the means of egress. Evacuation times are based on a linear relationship between number of seats and nominal flow time, with not less than 3.3 min for 2000 seats plus 1 s for every additional 50 seats up to 25,000. Beyond 25,000 total seats, the nominal flow time is limited to 11 min. Where nominal flow

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time refers to the flow time for the most able (ability to vacate) group of attendees, as some groups less familiar with the premises or less able groups might take longer to pass a point in the egress system. More contemporary design seeks a performance approach where design can be enhanced through the use of occupied time theories to reduce the anxiety of longer queuing times. Within the United Kingdom, there is momentum to progress past minute rules and evacuation times to looking at how to efficiently ensure all people can egress safely. The United Kingdom has Building Regulations, a series of Approved Documents which have been approved by the Secretary of State and contain practical guidance on how to meet said regulations (HM Government 2022). Current guidance states that buildings must provide appropriate provisions to warn of a fire, have the means for people to escape safely. They also state that all people should be able to “escape to a place of safety without external assistance” (HM Government 2022). As discussed in an article by Button (2023), this is important in terms of accessibility, however, is contradictory to other guidance clauses (Button 2023). For example, it is recommended that trained personnel will operate elevators, or that carry down procedures are acceptable. These contradictions have been made aware by the National Fire Chiefs Council, who have called for a review of the Approved Documents. Many suggest the use of elevators for evacuation. While the London Plan moves towards this idea, they still regard the use of external assistance (preference of an elevator driver over user-driven) the default method (Button 2023). Evidently, the United Kingdom has progressed to looking at the importance of equal egressibility rather than just broad egress minute rules. They are focused on ensuring all individuals are given the capability to assist themselves in egressing safely.

1.3 Introduction to Study Stadium This book addresses three primary gaps in knowledge regarding the effects of disabled people’s egress and safety in Canadian stadia. Knowledge as defined in Sect. 1.2 is limited due to (1) the current demographics of mobility related disabled persons in stadia and their specific accommodations, (2) lack of available speeds of mobility related disabled persons with associated simulations utilizing said data, and (3) lack of insights into how future growth in attending populations with accessibility needs may affect the overall design of the evacuation process. To address these research needs identified, the authors conducted a multi-year study of a professional level tennis stadium in Canada. The stadium considered herein is part of a multipurpose sport and entertainment complex that primarily hosts professional tennis in Canada in an annual summer tournament. Built in the early 2000’s, the stadium can seat 6316 within its main bowl, 1184 in its Level 2 concourse and provide an additional 4000 seats in temporary bleachers that can be erected on the top deck. The authors have been granted exclusive access to this stadium for study since 2018 for research purposes and at all stages research and recommendations have been given to stakeholders for relevant actions.

1.3 Introduction to Study Stadium

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Fig. 1.10 Stadium and sports village with areas of research interest

The area of study was the village space, a leisure facility set up outside the stadium bowl, as well as the stadium bowl. This multipurpose environment provides outdoor grounds for food and sporting vendors, and activities which the authors believed to be an attraction for a variety of persons with mobility requirements, particularly families and disabled persons. The stadium village and the stadium bowl at the time of study are represented in Fig. 1.10. The stadium village as of present date is not the same configuration and layout as that studied for the purpose of this book. The authors are currently studying the significance of these changes in a separate study at the time of writing, but this is beyond the scope of this book. Hence, as the layout has changed, the author’s do not identify the stadium’s name or specific location. Figure 1.11 illustrates the accessible features available at the considered tennis stadium on the main level. The top deck (located on the highest row of seating on the main level) is utilized as placements for wheelchair-accessible seating with approximately 200 seats reserved. For those with other accessibility requirements, there is seating located on the floor above the Main Level. An elevator is used to access both types of accessibility seating. Accessible washrooms, elevators and ramps are also highlighted. The stadium has two ramps as well as an elevator with four sets of restrooms (a second elevator was added in 2023). To reach the stadium, there are bus routes with stops just outside of the main entrance, accessible parking and an accessible subway station approximately 700 m walking distance from the stadium. While it is considered accessible to get to the stadium, at the time of study, the stadium had very few accessibility features which enable those with accessibility requirements to travel easily within the stadium. Subsequent renovations after the

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Fig. 1.11 Stadium’s accessible features on the main level8

study herein were completed during the covid pandemic to improve these features. Future studies by the authors intend to compare the stadium’s performance pre and post renovation but this is beyond the scope of the current study. The tennis event at this stadium attracts older persons, thus increasing the likelihood of mobility requirements. In Canada, the disability rate increases as the age increases with disability found in around 13% of those aged 15–24 years old and nearly 50% of those aged 75 and older (Morris et al. 2018). Additionally, the interactive activities and freedom offered by the village space were believed to be appealing for families with young children. The freedom associated with this spacious environment, particularly since it is on relatively flat grounds,9 was believed to be reasonably accessible for disabled persons. It should also be remarked that as the tournament progresses, the cost of ticketing also increases. This has important aspects related to socio-economic condition of those attending as disability is known to link to lower income and therefore a specific need to consider the proportion of those attending with mobility related disability throughout the tournament. While the applicability of the study in general can be applied worldwide, there are also uniquely Canadian aspects to consider which may not permit the quantitative

8

Accessible seating is obtainable through an elevator on the second deck (outer perimeter of drawing). Accessible washrooms are located one floor above were noted adjacent to the accessible seating. 9 Relatively flat grounds were defined using geodetic control points surrounding the stadium. The maximum incline angle was found to be approximately 0.67° with a maximum slope of 0.012 or 1.2%.

1.4 Ethics and Related Safety Limitations

25

and qualitative analysis of this study to be used elsewhere including cultural, behavioral, and weather aspects (Transport for London 2012). Of note is the lack of poor enforcement of Canadian legislation described earlier for accessible design, which may have a significant impact on the types of people in attendance. While the focus is on data collection to characterize accessibility considerations, the authors were conscious of preserving ethical considerations required by the university for filming and surveying.

1.4 Ethics and Related Safety Limitations Ethics clearance for the study herein was formerly granted by York University on the basis of internal university TD1/TD2 process as minimal risk. This denotes as a Thesis and Dissertation Proposal by student authors/researchers where ‘1’ and ‘2’ describes the Human Participants Research Protocol used. This ethics procedure is the same as that conducted through independent research projects involving human participants for non-thesis-based projects where an unique ethics number would generally be assigned. The authors note that different research institutions and different jurisdictions may have different ethics procedures which will require following and consideration for their own study. It is also of note that over time these procedures adapt at institutions accordingly and future studies may need additional consideration in the approval process. For example, at the Authors’ institution the TD1/TD2 process now produces a specific certificate number (as of 2023) as part of the clearance process. The procedure for the research presented herein was approved in the TD1/TD2 process and specified that: filming and survey permission from the stadium was granted, that standard information notices to patrons indicating that they will be filmed was performed, that ticketing indicated filming in progress, and that individuals were not readily identifiable in films or photos that would be published (hence image quality is downgraded for publication herein and altered to obscure facial reference), filming archives were to be stored externally, and that non-identifying information was collected in surveys with the right to withdraw at any time. It is important to also note that care was taken and reviewed by the stadium to ensure the members safety for filming and that of others. This mandated that filming was conducted using a ‘buddy’ system where all researchers were in pairs when filming. That all cameras were securely mounted so that they could not and did not fall from height. Subsequently, the research presented herein is largely observational and restrictions of camera angles would have to be considered and therefore influenced the data collection.

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References Ando, K., Ota, H., and Oki, T. 1988. Forecasting the flow of people (in Japanese). Railway Research Review,” 45(8), 8–144. Bergqvist, E. 2015. Studie av dimensioners inverkan vid utrymning i spiraltrappor (in Swedish). Bosina, E., Weidmann, U. 2017. Estimating pedestrian speed using aggregated literature data. Physica A: Statistical Mechanics and its Applications 468:1–29. Boyce, KE., Shields, TJ., Silcock, H. 1999. Toward the Characterization of Building Occupancies for Fire Safety Engineering: Capabilities of Disabled People Moving Horizontally and on an Incline. Fire Technol 35. Bukvic, O., Carlsson, G., Gefenaite, G. 2021. A review on the role of functional limitations on evacuation performance using the International Classification of Functioning, Disability and Health. Fire Technol 57:507–528. Button, M. 2023. Beyond Accessibility to Equal Egressibility. Design Fire Consultants. https:// www.designfireconsultants.co.uk/beyond-accessibility-to-equal-egressibility/. Carattin, E., Brannigan, V. 2014. Lost in abstraction: the complexity of real environments vs the assumptions of models. In: Fire and Evacuation Modelling Technical Conference. Gaithersburg. Chin, K., Young, T., Chorlton, B., Aucoin, D., and Gales, J. 2022. Crowd Behaviour in Canadian Football Stadia—Part 1—Data Collection. Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7). Chin, K., Young, T., Chorlton, B., Aucoin, D., and Gales, J. 2022. Crowd Behaviour in Canadian Football Stadia—Part 2—Modelling Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7). Chen, Y., Wang, C., Yap, J., Li, H., Hu S., Chen, C., and Lai, K. 2020. Fire Evacuation Process Using Both Elevators and Staircases for Aging People: Simulation Case Study on Personnel Distribution in High-Rise Nursing Home Discrete Dyn. Nature Soc, pp. 1–14. Employment and Social Development Canada. 2017. Accessible Canada—creating new national accessibility legislation: what we learned from Canadians. Employment and Social Development Canada. Folk, L., Gonzales, K., Gales, J., Kinsey, M., Carratin, E., and Young, T. 2020. Emergency Egress for the Elderly in Care Home Fire Situations. Fire and Materials (John Wiley). 44(4):595–606. Fruin, JJ. 1971a. Pedestrian planning and design. New York. Fruin, JJ. 1971b. Designing for pedestrians: a level-of-service concept. The port of New York authority 15. Gatien, S., Young, T., Khan, A., and Gales, J (2022) Pedestrian Behavior and Modelling for Commuter Airport Terminals. 6th FEMTC 8 pp. Gales, J., Champagne, C., Harun, G., Carton, H., Kinsey, M. 2022. Fire Evacuation and Exit Design in Heritage Cultural Centres. Springer Briefs in Architecture and Technology (Springer-Nature). 5 Chapters, 75 pp. Gales J, et al. 2020. Anthropometric data and movement speeds. SFPE final report. Geoerg P, Berchtold F, Gwynne, S. 2019. Engineering egress data considering pedestrians with reduced mobility. Fire Mater 43:759–781. https://doi.org/10.1002/fam.2736. Government of Canada. 2019. Accessible Canada Act—Loi canadienne sur l’accessibilité. Graat E, Midden C, Bockholts P. 1999. Complex evacuation; effects of motivation level and slope of stairs on emergency egress time in a sports stadium. Saf Sci 31:127–141. Grimard, B., Sinapi, S. 2021. Egress using stairs vs. elevators. Occupant Profiles and High-Rise Evacuation 1–16. Haghani, M. 2020. Empirical methods in pedestrian crowd and evacuation dynamics: Part II. Field methods and controversial topics. Saf Sci 129:104760. HM Government. 2022. The Building Regulations 2010: The Merged Approved Documents. Hoskins, B. 2011. No the effects of interactions and individual characteristics on egress down stairs. University of Maryland.

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Hurley, M. 2016. Engineering Data. In: SFPE handbook of fire protection engineering. pp 2429– 2528. Kinsey, M., Gwynne, S., Kuligowski, E., and Kinateder, M. 2019. Cognitive biases within decision making during fire evacuations. Fire Technology, vol. 55, no. 1, pp. 465–485. Kuligowski, E., Peacock, R., Wiess, E., Hoskins, B. 2014. Stair evacuation of people with mobility impariments. Fire Mater. Larsson, A., Ranudd, E., Ronchi, E. 2020. The impact of crowd composition on egress performance. Fire Saf J. https://doi.org/10.1016/j.firesaf.2020.103040. Larusdottir, AR., Dederischs, AS. 2011. A step towards including children’s evacuation parameters and behaviours in fire safety building designs. Fire safety science 187–197. https://doi.org/10. 3801/IAF. Lovreglio R, Ronchi E, Kinsey MJ. 2020. An Online Survey of Pedestrian Evacuation Model Usage and Users. Fire Technol 56:1133–1153. https://doi.org/10.1007/s10694-019-00923-8. Morris SP, Fawcett G, Brisebois L. 2018. A demographic, employment and income profile of Canadians with disabilities aged 15 years and over, 2017. Najmanová H, Ronchi E. 2017. An experimental data-set on pre-school children evacuation. Fire Technol 53:1509–1533. https://doi.org/10.1007/s10694-016-0643-x. Oasys. 2019. MassMotion Help Guide. London. Oliver, M. 2012. The new politics of disablement. Houndmills, Basingstoke: Palgrave Macmillan. Pauls J. 1984. The movement of people in buildings and design solutions for means of egress. Fire Technol 20:20. https://doi.org/10.1007/BF02390046. Rahouti A, Lovreglio R, Gwynne S. 2020. Human behaviour during a healthcare facility evacuation drill: Investigation of pre-evacuation and travel phases. Saf Sci 129. https://doi.org/10.1016/j. ssci.2020.104754. Ronchi E, Corbetta A, Galea ER. 2019. New approaches to evacuation modelling for fire safety engineering applications. Fire Saf J 106:197–209. https://doi.org/10.1016/j.firesaf.2019.05.002. Sano, T., Ronchi, E., Minegishi, Y., and Nilsson, D. 2018. Modelling pedestrian merging in stair evacuation in multi-purpose buildings. Simulation Modelling Practice and Theory, 85, 80–94. https://doi.org/10.1016/j.simpat.2018.04.003. Shi, L., Xie, Q., Cheng, X., Chen, L., Zhou, Y., and Zhang, R. 2009. Developing a database for emergency evacuation model. Building and Environment, 44(8), 29. https://doi.org/10.1016/j. buildenv.2008.11.008. Shiwakoti N, Gong Y, Shi X, Ye Z. 2015. Examining influence of merging architectural features on pedestrian crowd movement. Saf Sci 75:15–22. https://doi.org/10.1016/j.ssci.2015.01.009. Sørensen JG. 2013. Evacuation characteristics of visually impaired people—a qualitative and quantitative study. Fire Mater 39:385–395. Sørensen JG, Dederichs A. 2013. Equal access—equal egress: Accounting for people with disabilities in emergency. In: NNDR2013—12th Research Conference Nordic Network of Disability Research. Sørensen JG, Dederichs A. 2014. Evacuation from a Complex Structure—The Effect of Neglecting Heterogenous Populations. Transportation Research Procedia 2:792–800. Statistics Canada. 2019. Overweight and obese adults, 2018. In: Government of Canada. www.sta tcan.gc.ca. Accessed 20 May 2021. Templeton, A., Drury, J., and Philippides, A. 2015. From mindless masses to small groups: Conceptualizing collective behaviour in crowd modeling. Review of general psychology: Journal of Division 1, of the American Psychological Association, vol. 19, no. 3, pp. 215–229. Tversky, A., and Kahneman, D. 1974. Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. Thompson P, Wu J, Marchant E. 1997. Modelling evacuation in multi-storey buildings with simulex. Fire safety science 56:725–736. Transport for London. 2012. Station Planning Standards and Guidelines. https://docplayer.net/139 88764-Station-planning-standards-and-guidelines.html. Accessed 21 May 2021.

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Young T, Gales J, Kinsey M, Wong WCK. 2021. Variability in stadia evacuation under normal, high-motivation, and emergency egress. Journal of Building Engineering 40. https://doi.org/10. 1016/j.jobe.2021.102361. Young, T., and Gales, J. 2022. Towards Data-Informed Sub-Models for Pedestrian Microsimulation of Transportation Terminals. CSCE. Transportation specialty.

Chapter 2

Survey of the Importance of Accessibility Features in Stadia

Abstract A survey was conducted in which mobility-related disabled and nondisabled people, authority figures and employees, and officers were approached at random on stadia grounds during a professional tennis tournament. The first question asked the interviewee to list accessibility features in the considered stadium and its grounds, and the second question was to outline any suggestions they would make for future implementation of accessible designs in the stadium and its grounds. There were 93 responses for the first question, and 71 responses for the second. For the first question, elevators and ramps were the most common response, however, 19% of participants were unable to list an accessibility feature in the stadium and its grounds. Some of these participants were employees, officers, and other figures of authority, which would be a concern in emergency situations. For the second question, additional ramps and elevators were suggested. Also, there were suggestions to implement features such as shaded rest spaces which would benefit both mobility related disabled, persons with other reduced mobility conditions and non-disabled persons. Overall, this portion of the study demonstrated that improvements to the design of the stadium and its grounds can improve inclusivity and safety. As well, it shows that working with the population directly affected by the design of the environment can help to create universal spaces.

2.1 Introduction In various pedestrian studies of movement with data collection, there is benefit to survey the studied population as opposed to only collecting data on movement. Surveys may provide researchers with significant insights which can explain the data that is collected and reduce the subjectivity in the data’s interpretation. Prior to conducting a survey, care must be taken in its design and implementation (Ponto 2015; Gales et al. 2016). For example, the formulation of the questions must avoid bias and leading questions. Care in interpretation of the collected data is essential. The appropriate survey technique (mailed, phoned, in person etc.) also needs to be considered. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Gales et al., Egress Modelling of Pedestrians for the Design of Contemporary Stadia, Digital Innovations in Architecture, Engineering and Construction, https://doi.org/10.1007/978-3-031-33472-6_2

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In the study herein it was decided to leave the questions open ended as much as possible to gather a broad discussion from the attendees and avoid leading them to authors’ perceptions of what the stadium would require. It is known that the sampling of a population can be a challenge and the number of respondents potentially low to draw upon statistical significance in conclusions of the data (see Mazur et al. 2019). The population surveyed and resulting data collected herein is considered small as those attending the event were occupied with the tournament itself and will naturally choose not to participate in the survey. Those working on the grounds would also have duties and not the time to participate in answering. The survey also does not consider those who cannot attend the venue as well. As such, a broad statistical analysis will be prohibited in this case of the survey results. Other methods of data collection via survey (mailing etc.) would require personal information from the attendees which was considered prohibited based on the approved ethics of this study.

2.2 Survey Methodologies The survey herein was conducted to gain insight on people’s knowledge, experience, and opinions with regards to accessibility features offered throughout the complex. Two members of the authors’ research team spoke informally with spectators, stadium employees, and officers (definitions below). This includes collecting information on their opinions on all spaces and services involved with the event, the stadium and seven other courts, the pedestrian village, concession stands and promotor booths, washrooms, restaurants and lounges, parking facilities, transportation, etc. Both disabled and non-disabled persons took part in the survey to obtain responses from a diverse population. This was an important aspect of the study as it helped to further understand why disabled persons may be deterred from these types of events. The survey was conducted prior to the ticketing location as not to interfere with the populations use of the stadia at the Tennis event. The following lists the types of interviewees involved in the survey, and the importance of retrieving their respective responses. • Mobility related disabled persons: Those who are currently at the event can offer direct feedback on their experience with specific attention to their individual needs, and the kind of accommodations they would like to see implemented. • Non-disabled persons: Persons without accessibility needs often know someone who is disabled that may not be at the event and can therefore provide insight on why they have been deterred from attending, and how to implement better accommodations for participation in later events. This category may consider families with children. Children were not interviewed. • Authority figures and employees: Authoritative figures and employees to be informed of the available accessibility features to promote these features and further offer guidance to spectators.

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• Officers and other persons of authority: It is important for police officers, security guards, as well as employees (all of whom are authoritative figures to ensure public safety during the event of an emergency) to be knowledgeable of the diversity of human capabilities and thus the accommodations set in place to offer equitable mobile opportunities for disabled persons. Note that the decision in who to approach by the researchers was random. When approached, all interviewees were first notified of the interviewers’ identities and affiliation, the purpose of the study, the confidentiality involved with participation, and their right to withdraw at any given time. If permission to proceed with the survey was granted, the participants were then asked two formal opinion and open-ended based questions. Informal follow up questions were also asked for clarification or additional information about the interviewee. The first question was structured to examine the participant’s knowledge of accessibility features that are offered for the event. The second question then asked the participant to outline any suggestions they would make for future implementation of accessible designs. The conclusion of these two questions, they were given the opportunity to provide any further opinions and feedback, as well as ask any questions with regards to the study. Supplementary information was later recorded based on observation for demographic purposes and confirmed with the interviewee. This includes profile (do they have a mobility related disability), age group (young adult, adult, older persons), gender (male, female, other identity), whether they were part of a group, and any additional observations. The age of the respondent can fall into the appropriate category as identified by the respondent where young adult is 18–35, adult is 36–65, and older persons is over 65 (the senior classification in Canada).

2.3 Survey Observations and Results The discussion was structured to determine the extent of the public and staff’s awareness of the present accessibility features, and to understand ways of improving design for a universal environment with the direct input of the affected population. It is important to note that the survey regards the built environment only. It does not focus on the management and customer services of the event which can have potential to affect the experience of the attendee. Responses to both questions present an array of answers that explore many different influences on the overall attendance of persons with accessibility needs. Persons who participated in the survey were not limited to only one answer but were rather encouraged to speak freely and offer as much feedback as they wish. 93 responses were logged for Question 1 and 71 responses were logged for Question

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Fig. 2.1 Interviewee’s knowledge of accessibility features

2. These are presented and discussed below. Of those that participated in the survey, 24% were staff members of the stadium. Question 1: What accessibility features and accommodations are you aware of here at the Stadium? The first question was posed as above, to test the knowledge and awareness of the interviewee. Without influencing their responses, the interviewers left this question relatively vague and open for interpretation. This could mean individuals were responding with accessibility features they themselves used or ones know of but may or may not use. The collected responses were condensed and regrouped into more general categorical answers as follows (see Fig. 2.1). Upon observation, the interviewers logged additional notes with these responses, that often-suggested instances where the person was merely speculating accessibility features based on the presumption that a public venue should be able to offer said accommodations. Elevators and ramps are part of the main circulation route for the stadium. They were the most common responses because they are often the most physically obvious features that are used by non-disabled persons. The third most common response, however, was a simple idea that there are no accessibility features to be pointed out. This accounted for 19% of survey responders being unaware of any existing accessibility features. Question 2: What kind of improvements would you recommend be implemented with regards to overall accessibility and inclusivity at this stadium? The second question was posed as above, to allow the interviewee to express any concerns regarding a lack of accommodations and offer creative improvements that

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Fig. 2.2 Interviewee’s suggestions for implementing accessibility features

can cater to their specific needs, if applicable. This also allowed for some people to speak on behalf of someone they know, to suggest accessible features that may currently be deterring them from the event, and thus would promote attendance in the future. Similar to the responses for Question 1, these answers were condensed and regrouped in categories that reflect the general area of interest it improves, and further subcategorized by the specific improvement it entails as follows (Fig. 2.2). Figure 2.2 can be further refined in detail. 23 suggestions for ramps and elevators: more than just the two public elevators and one service elevator (6), having elevators at a more convenient and available location (4), making the use of elevators more visible (3), implementing ramps in place of steps at concession booths (3), installing escalators instead of stairwells in the stadium (3), installing ramps instead of stairs for seating in both bleachers and the main stadium (2), and increasing the width of stairwells (2). 10 suggestions for shaded and rest spaces: shaded area in the Pedestrian Village (5), shaded seats in the main stadium (3), and rest areas particularly in the Pedestrian Village (2). 9 suggestions for signs and navigation: implementing clear and visually obvious signs, as well as maps. 8 suggestions for parking: more convenient locations (4) and shuttle services (4). 8 suggestions for entrances: separate security line for persons with accessibility needs (5) and having more than just one main entrance (3). 7 suggestions to seating: selection for accessible seating for the games (5), as well offering chairs instead of just stools at vendors within the main stadium (2). 3 suggestions for restrooms: change stations in men’s washrooms (2) and having individual accessible stalls that are located outside of the main washrooms (1).

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2.4 Analysis and Discussion In the first question of the survey, the overall response tally further suggests that on average, people can only identify about one accessible feature available with all aspects of the event. This suggests that lack of attendance may be a result of lack of awareness, such that disabled persons have presumptions of inadequate accessibility and usability and are uniformed of architecturally accommodating designs that tailor to their specific needs. It was also evident that employees, officers and other figures of authority (those denoted as staff members) were often unaware of said features and therefore unable to offer adequate guidance for someone in need. This can be of concern in emergency situations. After analyzing the responses from the second survey question, it became clear that most feedback was both workable and relatively attainable from a design standpoint. However, a few responses are somewhat impractical in application. For example, some responses identified features which were already implemented at the stadium, revealing that the participant was simply unaware of them; this brings rise to the issues debated in response to Question 1. These responses included some of those regarding shuttle services from parking lots to the stadium grounds, implementation of ramps and elevators, accessible seating, and maps and navigation. In some of these instances, the participant acknowledged that these features are present, however still require improvements, suggesting that the current minimum standards that the stadium offers are inadequate in use. For example, the placement of the public elevators was suitable for the event, however, most issues resulted from spectators being unable to identify their locations. This, along with other issues, can be greatly improved by installing more effective signs, indicators, and maps. In addition, the second question responses demonstrated the extent to which the built environment can additionally affect non-disabled people. For example, to various degrees, prolonged sun exposure and fatigue impacts all people of this predominantly outdoor event. Thus, the second most common response helped to emphasize the importance of shade and rest spaces for all people. Integrating additional seats throughout the recreational grounds as well as covered areas both in and around the stadium to provide shade can positively impact the experience of all persons. Identified as the next most common suggestion, was the need for clearer and more obvious signs as well as easy-to-use and available maps. This may be because this issue affects all occupants. Overall, the survey study indicates that with the promotion of accessibility virtues, management, in theory, may be able to increase user satisfaction. Additionally, by implementing accessible features and improving certain aspects of design as mentioned above, stadia and public environments alike can improve inclusivity and overall safety. This also supported the notion that working together with the user population who is directly affected by the design of the environment is vital in identifying the need for and creating universal spaces.

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As a first stage study, this supports the benefits of data collection by use of surveys, interviews, and questionnaires for the persons under investigation to improve comprehension of human behavior and factors, and the implementation of accommodating designs. More specifically in terms of mobility related disabled persons, it also proves to be a successful method for future studies to employ extend mobility profiles beyond the visibly discernible disabilities and further understand the abilities and limitations associated with invisible disabilities.

References Gales, J., Folk, L., and Gaudreault, C. 2016. The Study of Human Behavior in Fire Safety Engineering using Experiential Learning. 7th Canadian Engineering Education Association’s Annual Conference. Halifax, Canada. 9 pp. Mazur, N., Champagne, R., Gales, J., and Kinsey, M. 2019. The Effects of Linguistic Cues on Evacuation Movement Times. 15th International Conference and Exhibition on Fire Science and Engineering. Royal Holloway College, Windsor, UK. 1903–1914. Ponto J. 2015. Understanding and Evaluating Survey Research. J Adv Pract Oncol. Mar–Apr; 6(2):168–71. Epub 2015 Mar 1. PMID: 26649250; PMCID: PMC4601897.

Chapter 3

Data Collection of Movement and Behaviour of Pedestrians in Stadia

Abstract In this chapter, the observational portion of the study is discussed. Three data collection trials were performed on three days (preliminary rounds, quarterfinals, and finals) in 2018. Video footage and stationary photos were taken and reviewed with each mobility case timestamped and logged in a data sheet. Movement profiles of mobility related disabilities (defined as individuals using mobility aids such as canes, wheelchairs, and crutches), those with reduced mobility (families with young children or those carrying oversized luggage), non-disabled persons, those visibly living with obesity, and those who consumed alcohol were generated. A total of 2397 mobility related cases which may affect movement were considered throughout filming which was approximately 3.46% of the total population. Of those mobility cases, 215 were disabled (0.31% of the total population). This percentage of the mobility-related disabled population is not reflective of the Canadian population (9.6% for Canadians aged 15 years and older identify as having directly a mobility related disability). As well, the total mobility cases were largely represented by those with reduced mobility which shows that disability is not a result of only physical requirements. Results from the movement speed profiles show that overall, nondisabled persons move faster than mobility related disabled persons. Additionally, those using assistive movement devices with wheels (for example, a wheelchair), moved faster than those without (ex. crutches).

3.1 Introduction There is value and use in the narrative to how a large field study is conducted. Often in research, only the final information of the study is presented. The scientific danger of not disclosing this narrative to how a large field scale study is conducted gives a misleading impression on budgetary and resources required for similar, replication and other future studies. There is also value in establishing how access was given to the authorship team at York University for replication purposes. This chapter therefore begins with a brief historical narrative of the authors’ experience with the study of stadia which began in 2016 and continues today. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Gales et al., Egress Modelling of Pedestrians for the Design of Contemporary Stadia, Digital Innovations in Architecture, Engineering and Construction, https://doi.org/10.1007/978-3-031-33472-6_3

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

Data collection of people behaviour and movement at stadia is not without difficulty. These exercises are rare primarily because of these requirements: ethical and safety considerations; human resources for data collection and analysis; cameras and equipment for data collection and storage; and other computational analysis technologies. This is in addition to the carefully established mutual relationship of trust with the stadium and staff. York University’s fire research group obtained Canadian Foundation for Innovation funding for the establishment of a Human Factors Lab which the authors use for their stadium study. The facility is well aligned with resources and technology to conduct these types of studies. As such this group currently maintains external storage ability requirements for two terabyte per study, four Canon 5Ds systems with EF zoom lenses and stands, and 50 Go-PRO units with associate grip technologies. This is complimented by a complete LiDAR system and modelling software (discussed for future research in automated data collection in Chap. 5). Through research collaborations with industry partner Arup, the research group utilizes and aids the development of current MassMotion evacuation software (discussed in Chap. 4). Subsequently as the group maintains a graduate program training fire engineers, up to eight student researchers at one time are available to assist for each field study at a given time. All trainees receive mentorship from consultants collaborating with the research group. The principal researcher for York University’s lab also has a reputation for publishing directly with professional sport associations regarding aspects of fire design and history of stadia design. A previous study involved a centennial analysis of the professional hockey team’s first arena that had a fire and was ultimately destroyed (Gales 2017). The access provided at the tennis stadium herein is one granted on the basis of the authors’ previous experience and reputation in studying stadia and the trust relationship with the stadia facilitators built with time. The authors at York University began their study of stadia in summer of 2016 with the aim of proof of concept to accurately measure exit flow seen in a professional baseball stadium. In that study, the authors experimented with various camera types and placements to resolve optimum viewing angles and required lenses and resolution of cameras. Figure 3.1 illustrates this first stadium study by the authorship team, specifically the stadium bowl with respect to accessibility. The study considered eight data collection visits to the stadia. In that study, the camera’s used at conclusion were Canon EOS 5D. These were found to be more in line with detail as opposed to the lower resolution Canon Rebels and GoPro cameras. The baseball stadium was a low attendance event (never above 50% capacity, often about 10% capacity) so meaningful conclusions regarding crowd densities were not applicable. The study did however illustrate the numerous research gaps that could be refined for study in larger and more dense stadia it also gave indication to the number of researchers required to collect data. Essentially, in order to maintain safety and protection of the equipment, it was determined that two researchers were needed for every camera location being utilized for data collection. The study was utilized as a proof of concept to procure funding from the NSERC Collaborative and Development (now Alliance) program

3.1 Introduction

39

Fig. 3.1 Author’s original study of stadium in 2016 where exit flow was monitored which included mobility related disabled persons

in collaboration with the Toronto office of Arup which then led to a follow-on study in 2017 of a larger stadium. Building upon this relationship with the stadium, the authors then were able to access a football stadium to record higher densities and pedestrian flow using cameras and protocols established in the baseball study. That study was published by Chin et al. (2022a, b). That study had a significant limitation in that the camera placement locations were restricted so as not to interfere with concessions which were also in the stands. This then limited some of the resolution necessary to study accessibility considerations fully. The study also allowed the authors to experience weather effects on data collection and protection of equipment. More pronounced in learning the study gave indication to the number of researchers required for manual analysis of the collected data in order to minimize subjectivity in findings. By late 2017, the authors were then able to have access to study a heritage stadium. At this stadium, filming rights were heavily restricted; they could not be performed during the professional match as these rights belonged to a third-party broadcaster and not the stadium itself. This made observations purely qualitative as only a portion of the ingress and egress could be studied. The previous studies showed that large portions of populations enter after the game begins and leave before the end of the game. Flow rates and densities being collected were essentially meaningless as the previous study indicated that a large proportion of people arrive after the game commences and before the game concludes so only partial information could be collected. The heritage stadium had architectural similarities with those stadia seen

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

Fig. 3.2 Heritage stadium opened in 1915

to develop the Green Guide (see Figs. 3.2 and 3.3). With several stadia studied and experience built by the researchers the authors were then able to have access to the professional tennis stadium in 2018 and 2019 (introduced in Aucoin et al. 2019; Young et al. 2021 respectively). A planned 2020 study was delayed due to the covid pandemic and will now commence in 2023 following the publication of this book. The authors have declined the study of stadia in the past. A costing exercise to the number of resources required for data collection and analysis may determine that the study will not have the available resources. There is great care required when resourcing stadia data collection and analysis exercises so as not to over commit. Without automated and Artificial Intelligence driven tools the timing of collecting information on one agent or pedestrian can be the order of minutes multiplied by the capacity of the stadium. With professional and even academic billing, such studies may not be feasible to conduct.

3.2 Data Collection Methodologies for Movement and Behaviour …

41

Fig. 3.3 Person using crutches in heritage stadium grounds

3.2 Data Collection Methodologies for Movement and Behaviour of Pedestrians Following the survey study described in Chap. 2, an observational study was performed at the professional tennis stadium. The intent of this research phase was to collect data and analyze pedestrian movement, human behavior, and crowd demographics at a seven-day international professional tennis tournament. Specific focus was placed on mobility related disabled persons. Authors used visuals only and the procedure was therefore not holistic at identifying profiles of persons with non-visible mobility related disabilities. Three data collection trials were performed on the second, fifth, and seventh day for the preliminary rounds, quarterfinals, and finals respectively. All three days were sunny with an approximate daily temperature between 25 and 28 °C, apart from rainfall for an hour during the second day. Survey data collection and filming days did not overlap due to limited human resources. Movement data was collected with video films and stationary photos, later to be analyzed for visually discernable mobility cases. Each instance was timestamped and logged in Excel datasheets to create mobility count and profiles of analysis. These profiles were categorized by a variety of mobility requirements relating to disability, and other reduced mobility conditions. Mobility requirements relating to disability describe the mobility cases have been limited to those of which are visually discernable, often by use of a mobility device. This includes the use of a

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

cane, crutches, electric wheelchair, manual wheelchair, mobility scooter, rollator, walking stick, or assistance by another person (see Fig. 3.4 for an example). Other reduced mobility conditions describe the mobility cases that present some form of reduced mobility condition that is not a result of a mobility related disability. Families with young children were identified and recorded when a person was found responsible for the guidance of one or more children. Oversize luggage and roller suitcases were recorded for persons hauling freight that appeared to be overbearing or restrict their ability to move. Any other situation in which an individual appeared to represent a degree of immobility not identified above was noted in ‘other mobility cases’ category. This profile most often included persons in motorized shuttles and mascots.

Fig. 3.4 Person using a mobility scooter and a rollator from village space footage of tennis stadium

3.2 Data Collection Methodologies for Movement and Behaviour …

43

High-resolution Canon EOS 5DS (50.6 megapixels) cameras with 24–105 mm lens were stabilized in fixed conditions at an elevated location (approximately 13.5 m in elevation and 45 m away from the furthest measurement taken) on the stadium upper concourse giving an unobstructed view of the village space. The cameras recorded film at 30 frames per second. This position enabled clear visualization of pedestrian movement through the village ground as seen in Fig. 3.1. Manual groundssurveying by the authors was performed to measure the location of defining features on the ground (pavement markers and natural features) and these positions were then calibrated on an as-built CAD drawing of the stadium grounds with known and precise measurements. Derived movement speeds considered the person’s position and travel path as tracked linearly through their trajectory from origin into the camera’s view to the person’s end destination (over at least 20 m of recorded travel distance), using the person’s foot overlaid on identifiable markers and natural features surveyed. The end destination was one of four possible places: the alcohol grounds entry, the restaurant area, entrance to the maintenance grounds, or the corridor into the main village (labeled on Fig. 1.10). Overlaying travel points in the provided CAD, travel distance of a person was measured by linear paths between points. The movement speed measured represents the average speed through this path. Another analysis was considered using the same camera to record people entering and exiting an alcohol tent. This film was specifically used to help the authors investigate how ‘any’ consumed alcohol could affect the movement behaviour of individuals. A 2 m wide by 6 m long corridor was monitored where people would enter and exit from the alcohol drinking area. The time through this corridor was used as part of the speed calculation. In this case the travel distance was smaller and more representative of a spot speed measurement. Using the recorded time on screen for each person and these pre-calculated distances for the respective routes, a walking speed (m/s) for each person was theoretically calculated. In addition to the mean speeds, the minimum, median, and maximum horizontal speeds for each movement profile are tabulated along with standard deviation. This is in line with the format of movement data supplied by the SFPE (Society of Fire Protection Engineering 2016) and quoted elsewhere for use in evacuation and pedestrian modelling. To study movement on stairs, the stairs inside the stadium bowl were analyzed using the same camera. These are the steps that are designed in the stands for spectators to reach their seat. The dimensions provided in CAD drawings were verified and confirmed with manual measurements to be implemented into the calculations of this study. The vertical and horizontal distances were used as 0.135 m and 0.419 m respectively, with a gradient of 17.9°. The stairs consisted of up to 54 consecutive steps with a single railing placed in the center of the stair. Overall, 29 h of film footage was collected. The assistance of eight student trainees was needed to record footage and analyze film for data extraction. To reduce factors that have the potential to skew the database, all recorded points were carefully reviewed by multiple student researchers and verified by the principal investigator. Before conclusive data was calculated, points that presented themselves as potential outliers were qualitatively re-evaluated in terms of their validity to the study and removed if necessary. In multiple cases where the a person was loitering, standing

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

in queue for vendors, or waiting for others, for example, the data point was deemed insignificant to the overall analysis and furthermore removed from calculations. This process rejected about 6.6% of what was determined to be insufficient data points from the original set. Future research could consider the use of an overhead camera on the grounds. Technology such as a spider camera, LiDAR or drone footage could be considered (in the authors case this was not permissible by stadium management in the current study). These and other technologies will be discussed in Chap. 5. Alternative filming locations could also be considered in the grounds of the stadium where more identifiable markers are present and where surveying may be used to develop an automated approach to data collection such as spot speed recognition using kinematic software. It is acknowledged that the camera being at an angle introduces a substantial time requirement in data production for researchers hence the data profiles taking more than five years to generate and the use of multiple researchers to compare results and reduce subjectivity. Each mobility case was further subcategorized by age group to allow for demographic analyses. Age was divided into four groups representing “children”, “young adults”, “adults” and “older persons”. Specific ages being defined would introduce too much subjectivity as these individuals were not surveyed for exact age. Age category was determined based on appearance in conjunction with mobility. As it was both impractical and beyond the scope of ethics clearance to survey each person tracked, the profile classification process was done visually based on several factors to estimate age and mobility following a methodology presented within Mazur (2019). For example, Mazur et al. defines approximate age criteria for an older person as having grey or white hair, or no hair; they move slowly as compared to other visitors, perhaps with assistance (i.e., cane, walker); their facial features are aged (i.e., wrinkles, lines, etc.). They define children as being under the supervision of an adult. In some cases, a person’s circumstance could easily be determined by mobility devices used such as wheelchairs or crutches. However, as most of the population does not fall into a person of reduced mobility profile, other factors needed to be considered as well for categorizing the attendees. The authors and research team also considered height differences and group movement with older adults (i.e. parents) to identify Children, younger clothing and hairstyles along with behavioral cues such as smartphone usage and “selfies” to identify Young Adults, and older clothing styles and walking behaviour to identify Older persons. Those who did not fit any category based on the considered factors, were classified as Adults. To reduce any subjectivity, the authors and research team carried out an independent analysis, by multiple members who have worked previously on other data collection projects identifying ages with known age data (see Gales et al. 2022; Mazur 2019 for methodology detailing). After comparing the results from the authors and research team, very few differences within the data were found. Alcohol consumption movement profiles do not reflect the amount of consumption, only that the person entered and exited the tent area and consumed ‘any’ alcohol while in this area. Consumption confirmation was made, but the volume and concentration amount or what was being consumed was not possible. Separate tents on the

3.3 Mobility Cases

45

grounds are present for eating purposes. Persons visibly living with obesity movement profiles are estimated by body dimensions relative to that of persons visibly living without obesity individual by at least a factor of 1.5 within the 2 m corridor shown. While it is possible to indicate persons living with obesity, it is acknowledged that this definition will be subjective in nature.

3.3 Mobility Cases The event’s overall attendance was provided by the admissions office as 150,597 spectators over the course of seven days. Based on the turnout of each game visually present in the stadium bowl, the daily attendances are estimated as produced in Table 3.1. The total population of interest is therefore 69,276 for the three days when filming occurred. It is noted that these statistics are limited to the approximation of expected attendance and only account for spectator admissions. Whereas the recorded mobility database herein will account for a population beyond the count of ticket admissions, including employees, athletes, technicians, etc. Notably, person movement profiles for oversized luggage and other accessibility devices are governed by what appears to be employees maneuvering through the village space to deliver freight. A total of 2397 mobility cases which may affect movement were considered from the footage analysis on the village space over the three filmed days. From the resulting data points, the frequencies of mobility profiles have been summarized in percentages relative to both the overall stadium population, and the summation of all recorded mobility cases in Table 3.2. The frequency of each mobility profile in relation to the total population recorded uses the total attendance (n = 69,276). Family with Children frequency is based on the entire group size (i.e. a family of four represents a frequency of four). Future research should consider the sample Table 3.1 Estimated daily attendance Game day

Game schedule

Number of matches

Estimated percent of overall attendance (%)

Attendance

1

1st round

2

10

15,060

2a

1st round

2

10

15,060

3

2nd round

2

12

18,072

4

3rd round

2

14

21,084

5a

Quarter finals

2

16

24,096

6

Semi finals

2

18

27,108

7*

Finals

1

20

30,120

100

150,597

a

Game days used for experimental trials

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

Table 3.2 Percent population presenting with reduced mobility Mobility profile

Frequency Relative frequency to total Relative frequency to total mobility cases (n = 2397) population (n = 69,276) (%) (%)

Cane

41

1.71

0.06

Crutches

5

0.21

0.01

Mobility scooter

21

0.88

0.03

Person requiring assistance

61

2.54

0.09

Rollator

13

0.54

0.02

Walking stick

20

0.83

0.03

Wheelchair (electric)

13

0.54

0.02

Wheelchair (manual)

41

1.71

0.06

Persons with mobility related disabilities

215

8.97

0.31

Family with children

1170

48.81

1.69

Oversize luggage

849

34.42

1.23

Roller suitcase

24

1.00

0.03

Other

139

5.80

0.20

Persons with other reduced mobility conditions

2182

91.03

3.15

100.00

3.46

Total observed mobility 2397 cases which may affect movement

Bold indicates a summation of mobility profiles

movement and density with respect to companions of other mobility related disabilities or reduced mobility. Future research should also consider the differentiation of the children category by considering the effect of toddlers and infants specifically on the effect of speed of the family with children category. Due to the nature of the village offering recreational facilities, which is not a mandatory space to a person’s experience at the event, not all persons accessed the observed space whereas some individuals can circulate the entry and exit point multiple times. Care was taken to detail people to reduce the likelihood of the database exhibiting an individual more than once.

3.4 Movement Speed Profiles of Pedestrians From the data collected surrounding the population distribution, movement profiles were created for specific demographics. Movement speeds are specifically for nondisabled persons (on both level ground and on stairs), mobility related disabled

3.4 Movement Speed Profiles of Pedestrians

47

persons (on level ground), those visibly overweight (on both level ground and on stairs), as well as those affected by alcohol consumption (on level ground). All movement profiles were determined using unimpeded movement, meaning the crowd density was not restricting the movement speed measured. The movement profiles are unimpeded meaning that do not account for speed reductions that would be seen in fundamental diagrams. That reduction trend will need future study to compare to current fundamental diagram speed reductions in use. That is beyond the current scope of this research. The following tables show the movement profiles derived for non-disabled, unimpeded persons on both level ground (Table 3.3) and for stair movement (Table 3.4). The Fruin profile (a default movement speed often used) was added in Table 3.3 for comparative purposes. Movement Profiles are defined as agent profiles as persons are defined as agents in evacuation modelling software. Table 3.5 shows the mobility related disabled profiles for those on level ground. This includes people using assistive movement devices. Table 3.6 shows the movement profiles for other reduced mobility profiles. These include those in family groups, handling oversized luggage, roller suitcases and others. Tables 3.7 and 3.8 show the movement data collected for those visibly living with obesity on both level ground (Table 3.7), as well as ascending and descending Table 3.3 Non-disabled profiles for unimpeded horizontal movement on level ground Agent profile

Sample size

Speed (m/s) Min

Max

Mean

Median

SD

Child

52

0.34

5.04

1.45

1.30

0.75

Young adult

50

0.71

3.92

1.61

1.52

0.58

Adult

51

0.67

3.53

1.64

1.65

0.59

Older persons

50

0.40

2.52

1.32

1.23

0.48

Fruin

–

0.65

2.05

1.35

–

0.25

Table 3.4 Non-disabled profiles for unimpeded stair movement (stair gradient = 17.9°) Agent profile

Sample size

Horizontal speed (m/s) Min

Adult Older persons

Max

Mean

Median

SD

Direction

54

0.42

1.40

0.77

0.72

0.20

Ascent

53

0.36

1.26

0.71

0.70

0.18

Descent

51

0.16

1.14

0.55

0.55

0.15

Ascent

50

0.16

0.96

0.50

0.52

0.18

Descent

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

Table 3.5 Mobility related disabled profiles for unimpeded movement on level ground Sample size

Agent profile

Speed (m/s) Min

Max

Mean

Median

SD

Cane

41

0.21

1.68

0.91

0.88

0.28

Crutches

5

0.35

1.22

0.68

0.66

0.34

Mobility scooter

21

0.57

2.71

1.39

1.47

0.45

Person req. assist

61

0.16

2.02

0.98

0.95

0.41

Walker (rollator)

13

0.21

2.02

1.07

0.98

0.59

Walking stick

20

0.14

1.68

1.01

1.04

0.41

Wheelchair (electric)

13

0.06

1.76

1.08

1.01

0.46

Wheelchair (manual)

41

0.06

3.54

1.17

1.10

0.50

Total

215

0.06

3.54

1.05

1.02

0.44

Table 3.6 Persons with other reduced mobility profiles for unimpeded movement on level ground Agent profile

Sample size

Speed (m/s) Min

Max

Mean

Median

SD

Family group

1170

0.06

5.04

1.11

1.24

0.54

Oversize luggage

849

0.08

4.72

1.50

1.45

0.55

Roller suitcase

24

0.40

2.71

1.64

1.67

0.55

Other

139

0.19

7.08

1.82

1.55

1.06

Total

2182

0.06

7.08

1.39

1.36

0.61

Table 3.7 People visibly living with obesity—profiles for unimpeded movement on level ground Agent profile

Sample size

Adult

47

Speed (m/s) Min

Max

Mean

Median

SD

0.60

3.85

1.30

1.14

0.54

Older persons

31

0.46

3.37

1.21

1.04

0.63

Adult (without obesity)

51

0.67

3.53

1.64

1.65

0.59

Older persons (without obesity)

50

0.40

2.52

1.31

1.23

0.48

Total (visibly living with obesity)

78

0.46

3.85

1.27

1.13

0.58

on stairs (Table 3.8). Table 3.9 compares the movement speed of those who have consumed alcohol to those who have not. These movement profiles will be used in the next chapter to simulate various modelling scenarios. This will aid in the understanding of the future accessibility levels of the current stadium in egress. As well, the mobility related disabled population’s movement will be able to be seen from current demographics, and then also with an increased mobility related disabled population.

3.5 Analysis and Discussion

49

Table 3.8 People visibly living with obesity–profiles for unimpeded stair movement (Gradient = 17.9°) Agent profile

Sample size

Horizontal speed (m/s) Min

Adult Older persons

Max

Mean

Median

SD

Direction

54

0.17

1.57

0.62

0.58

0.23

Ascent

54

0.13

0.94

0.50

0.51

0.18

Descent

54

0.06

0.88

0.42

0.46

0.18

Ascent

54

0.14

0.78

0.40

0.39

0.14

Descent

Table 3.9 Unimpeded movement before and after any alcohol consumption on level ground (adults and older only) Agent profile

Sample size

Speed (m/s) Min

Max

Mean

Median

SD

After consumption

50

0.34

1.88

1.02

1.02

0.39

Before consumption

50

0.67

3.53

1.64

1.65

0.59

One important consideration that requires commentary is that some profiles that were recorded deviate significantly from the distribution of the average. For example, instances where 5 m/s are recorded. In these extreme cases these profiles were omitted when calculating mean and median movement profiles but still reported as maximum values in the data sets.

3.5 Analysis and Discussion From the mobility cases seen during the observational study, a comparison between the present population of mobility related disabled persons, the present population of other reduced mobility conditions, and the Canadian population statistics of mobility-related disabled persons can be made. From Tables 3.2 and 3.10 it is evident that the population of mobility-related disabled persons (0.31% of the total stadium population) is not reflective of the Canadian population (9.6% for mobility related disability of Canadians aged 15 years and older) (Morris et al. 2018). It also reveals that disability is a result of not only physical requirements (8.97% of total mobility-related disabilities), but in fact affect those with other reduced mobility conditions significantly more (91.03% of reduced mobility cases, 3.15% of the total population). To understand the distribution of reduced mobility by age group and locate a gap in attendance from disabled persons attendance gap, the results presented in Tables 3.2 and 3.10 requires further demographic analysis. Figure 3.5 therefore illustrates the prevalence of reduced mobility for the predefined age groups by filming day. The frequencies of mobility cases recorded are labelled within the bar, accompanied by

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3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

Table 3.10 Percent population presenting with reduced mobility Mobility profile

Frequency

Relative frequency to total mobility cases which may affect movement (%)

Relative frequency to total population (%)

Persons with mobility related disabilities

215

8.97

0.31

Persons with other reduced mobility conditions

2182

91.03

3.15

Total observed mobility cases which may affect movement

2397

100.00

3.46

Bold indicates a summation of mobility profiles

the respective percent proportions relative to the entire population offset in brackets to the right. Part (a) consists of all recorded reduced mobility cases, whereas part (b) is specific to those relating to mobility related disabilities specifically. Although available statistics suggest that disabilities are most prevalent with increasing age (Morris et al. 2018), the results presented in Fig. 3.5 (a) show that the governing populations of reduced mobility cases exist significantly within the adult population. This proportion is high because of the three most significantly reduced mobility profiles which primarily impact that of adults: families with young children (n = 1170), persons with oversize luggage who were most often employees (n = 849), and other cases who were also most often employees (n = 139). Unlike the demographic of all persons with reduced mobility, the proportions presented in Fig. 3.5b are more aligned with the expectation that the prevalence of disabilities increases with age (Morris et al. 2018). Additionally, it is seen in both figures that the amount of mobility restrictions recorded in Day 2 are almost twice that of any following trial day. This is believed to be a result of multiple factors associated with the events hosted on these days. As mentioned earlier, Day 2 was performed during the preliminary rounds whereas Day 5 and 7 were performed during the quarterfinals and finals. Major differences include the cost of admissions, which increases as the tournament progresses, as well as crowd size which proved to nearly double that of Day 2 for Day 5 and Day 7. This suggests that, while not the leading factor, public participation of reduced mobility persons may have a relationship to socioeconomic trends, such that mobility related disabled persons are shown to have higher unemployment rates and lesser incomes (Tricco et al. 2017; Morris et al. 2018). Therefore, both families with young children and disabled persons may be more influenced by affordability. These two groups may also be influenced by the crowd size, such that less busy environments are more desirable. A study by Blockmans (2015) showed there are disability stereotypes which often govern social interactions between disabled and non-disabled persons. These can include an “elephant in the room” tension or the “shush” rule in which parents try to control their children’s questions or reactions. Often this results in disabled persons feeling uncomfortable in crowds (Blockmans 2015). With this, the authors justify the importance of further studying the causation of low public

3.5 Analysis and Discussion

51

Number of Persons with Reduced Mobility

1200 71 1000 800 600

879

21

26

460

424

185

146

160

Day 2

Day 5

Day 7

400 200 0 Young Adult

Adult

Older

Number of Persons with Physical Disabilities

a) 120 100 80

57

60 20

40 52

20

20 0

37 5 Day 2

17 3 Day 7

1 Day 5 Young Adult

Adult

Older

b) Fig. 3.5 Demographics of a all reduced mobility cases and b mobility related disabilities specifically

participation of disabled persons and how to relieve these barriers for engineered designs to exceed the current minimum standards of accessibility. Of the 29 h of film that were analyzed, a total of 2397 people presented with reduced mobility which requires some form of accommodation. In addition to this count, the likelihood of an even greater population experiencing reduced mobility at the event was noted due to the limitations of data collection. Because data analysis was conducted by way of visual observation, invisibly disabled persons presenting are not accounted. Therefore, to create universal designs, the many cases of reduced mobility must be understood to identify and remove debilitating barriers to not only increase social involvement of persons with mobility requirements, but also ensure their safety and equal opportunity. Using the values from Tables 3.3 and 3.4, observations can be made by comparing the movement profiles of non-disabled and disabled persons. As seen in Table 3.3, the

52

3 Data Collection of Movement and Behaviour of Pedestrians in Stadia

adult population is seen to move with the fastest average speed on level ground with the older demographic moving at the slowest average speed. Note that the average speed of non-disabled persons on level ground from all demographics excluding older people are faster than the Fruin average speed, and the ascending speeds for pedestrians on stairs is faster than descending, which is unexpected though the difference is small. The authors hypothesize that this observed trend may be influenced by sun glare on the concrete stairs. Efforts were not made to construct fundamental diagrams with density for crowd influence on speed but could be considered in future studies. From Table 3.5, it can be seen that those using a cane, crutches, walking stick, or a person requiring assistance moved at a slower rate than the others. The common factor separating the slower demographic from the faster demographic is that those who move faster generally use an assistive movement device that has wheels, while the slower do not. Table 3.6 however, shows the movement profiles of other reduced mobility cases on level ground. Some observations from this dataset include the slower speed of the family group demographic compared to the others. This decrease in movement speed can be accounted for due to the presence of children or older persons. As seen in Table 3.3, the movement speed of children and older demographics is slower than the adults and young adults. It is known that when moving in a group, often the slowest moving individual will dictate the speed of the group. Furthermore, when comparing the data from tables, the overall standard deviation and average speed between the varying profiles in Table 3.6 is higher than in Table 3.5. In comparison to data collected by Boyce et al. (1999), the data collected in the study herein (Table 3.5) was slower for those using crutches. For those with walking sticks, walkers, electric and manual wheelchairs, the population moves faster on level ground. It is important to note that the standard deviation for the crutches and walking stick were close. In the Boyce et al. (1999) study, the standard deviation for electric wheelchair users was not calculated as they had a sample size of two (Boyce et al. 1999). This indicates that further studies with larger sample sizes in varying structures should be completed to reduce uncertainty and illustrate statistical significance. Next, data from Tables 3.7 is discussed. When comparing the data for individuals living with obesity versus without obesity on level ground, the speed of those overweight is slower than those who are not. Table 3.9 shows that ‘any’ alcohol consumption does have an impact on an individual’s walking speed, making them walk slower than normal. Overall, those who are visibly living with obesity, or have consumed alcohol move similar to disabled persons as seen in Tables 3.5 and 3.6 in the sense that it slows movement speeds. The three different egress motivational conditions and their stimuli introduced in Chap. 1 and comprehensively presented in Young et al. (2021) illustrated several key findings important in association to the above data. The egress of stadium stands differs depending on the nature of the motivational scenario, but evacuation is influenced by actions and directions of authority figures. Movement through the stadia is heavily influenced by normality, attentional, optimism, and bandwagon behaviors.

References

53

During a standard post-game egress, spectators are more likely to take longer to initiate their egress if post-game activities are still occurring on the stadium’s field. This can cause congestion on passageways as people may stop to congregate. Despite egresses taking longer in normal conditions than would be specified in guidance, there was no evidence of visible anxiety in the spectators in this process. During high motivation scenarios, faster evacuation and higher traffic volumes were observed with greater congestion at exits being observed compared to normal evacuation. The differences between the high-motivation rain event and the fire events were potentially influenced by the state of play on the field and associated perception of urgency to evacuate, as play was not stopped during the fire event observed. Furthermore, there was an absence of a dominant authority figure ceasing the activities during the fire event; and that the high motivation rain event was found to have lower premovement, faster walking speeds, and a shorter evacuation time despite the higher volume of people. Differences here could be attributed to longer pre-movement times in the fire scenarios, which can be further attributed to formed social identities in the crowd (those that investigate, those that leave, and those that participate in vandalism for some examples). The authors are currently evaluating the film data from the rain event to consider the speeds observed of those with mobility related disabilities. Though at the time of publication of this book that data is not yet available. The field data collected has been comprehensively gathered to determine and reveal qualitative behavioral data and quantify egress pattern data. The data has been post-processed to minimize bias in interpretation. The data is indeed helpful now to form initial input assumptions and to design stadia egress and evacuation models. However, this is a first stage study, meant to be built upon where future research should consider utilizing data from multiple scenarios to confirm the observations seen. A database of case studies across a diverse set of hazards and egress conditions, behavioral observations, and movement speed profiles would be immensely useful in reducing uncertainty around future predictive egress and evacuation modelling where validation and verification will be needed. This is recommended as a second stage of this research.

References Aucoin, D., Young, T., Kinsey, M., Gales, J. 2019. Modeling Human Behavior in Emergency Stadium Fire Evacuations. In: Interflam 2019: 15th International Conference and Exhibition on Fire Science and Engineering. pp 659–704. Blockmans IGE. 2015. Not Wishing to Be the White Rhino in the Crowd: Disability-Disclosure at University. J Lang Soc Psychol 34:158–180. https://doi.org/10.1177/0261927X14548071. Boyce, K. E., Shields, T. J., and Silcock, H. 1999. Toward the Characterization of Building Occupancies for Fire Safety Engineering: Capabilities of Disabled People Moving Horizontally and on an Incline. Fire Technology, 35(I). Chin K, Young T, Chorlton B, Aucoin D, and Gales J. 2022a. Crowd Behaviour in Canadian Football Stadia—Part 1—Data Collection. Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7).

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Chin K, Young T, Chorlton B, Aucoin D, and Gales J. 2022b. Crowd Behaviour in Canadian Football Stadia—Part 2—Modelling Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7). Gales J, Champagne C, Harun G, Carton H, Kinsey M. 2022. Fire Evacuation and Exit Design in Heritage Cultural Centres. Springer Briefs in Architecture and Technology (Springer-Nature). 5 Chapters, 75 pp. Gales, J. 2017. How a fire changed the course of NHL History. Induction Ceremony Legends. The Hockey Hall of Fame. 68 – 78. Published as part of the NHL centennial celebrations. Morris SP, Fawcett G, Brisebois L. 2018. A demographic, employment and income profile of Canadians with disabilities aged 15 years and over. https://www150.statcan.gc.ca/n1/pub/89654-x/89-654-x2018002-eng.htm. Mazur, N., Champagne, R., Gales, J., and Kinsey, M. 2019. The Effects of Linguistic Cues on Evacuation Movement Times. 15th International Conference and Exhibition on Fire Science and Engineering. Royal Holloway College, Windsor, UK. 1903–1914. Tricco AC, Lillie E, Zarin W. 2017. Insights on Canadian Society Low income among persons with a disability in Canada. Young T, Gales J, Kinsey M, Wong WCK. 2021. Variability in stadia evacuation under normal, high-motivation, and emergency egress. Journal of Building Engineering 40. https://doi.org/10. 1016/j.jobe.2021.102361.

Chapter 4

Evacuation and Pedestrian Modelling in Stadia

Abstract In this chapter, techniques and considerations for reducing uncertainty in the modelling the movement of stadium attendees are discussed. The current generation of commercially available pedestrian modelling revolves around agent-based social force models (the Artificial Intelligence of the movement model so to be). The creation, programming, and modifications of the modelled environment and movement engine are critical to overcoming uncertainties. These theorems and algorithms are discussed with a presentation of historical narrative on the subject. Benefits of using project-specific data to reduce uncertainty in the models are demonstrated, and the potential and needed considerations for extending the model through Software Development Kit capabilities are discussed. By understanding the impacts of programming and modifications, more models with higher certainty can be built which can include consideration for disabled persons. The construction of a stadium model will be explained with appropriate limitations described. A suite of scenarios will be considered to explore future demographic trends in stadium and their effect on evacuation.

4.1 Introduction The purpose of this chapter is not to present a verification or validation of evacuation modelling software. This is described elsewhere for stadium pedestrian movement modelling (Chin et al. 2022a, b) and other infrastructure types with potential for high crowds and diverse demographics (Gales et al. 2022). Herein, the authors illustrate the use of the collected movement data from Chap. 3 for higher certainty pedestrian evacuation models based upon realistic population demographics—particularly those with mobility related disability that affects movement. This discussion then allows practitioners to consider the design of future structures to enhance safety by considering the needs of the population with more certainty in the pedestrian movement models.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Gales et al., Egress Modelling of Pedestrians for the Design of Contemporary Stadia, Digital Innovations in Architecture, Engineering and Construction, https://doi.org/10.1007/978-3-031-33472-6_4

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There are several types of uncertainties that need discussion in pedestrian modelling endeavors. These include measurement, parameter, and modelling uncertainties (SFPE 2019), all of which are discussed and reviewed in context: The measurement uncertainty, which regards the accuracy of the collected data; The modelling uncertainty, which regards the assumptions of the actual behaviour of persons and the ability of the software to demonstrate this behaviour, dependent on the completeness of real-world human behaviour being addressed; and the parameter uncertainty, which regards the accuracy of the inputs into the model, individual speed for example. These are all of important discussion when attempting to achieve the goal of enhancing safety by utilizing pedestrian modelling tools.

4.2 Artificial Intelligence Theorems in Pedestrian Modelling Currently many of the frequently utilized evacuation and pedestrian modelling software relies upon a modified social force movement theory. This is the Artificial Intelligence (AI) mechanic which governs the movement of the virtually represented persons or ‘agents’ through the modelling space of the pedestrian software. This sub section will describe the theory and the ongoing attempts to improve upon the AI being utilized for movement behaviour in these models. This section presents a historical narrative of the Social Force Theory to the reader with explanative description. As the below narrative will describe (see Fig. 4.1), while these research efforts are making significant progresses moving away from the original theory and adapting it, one trend in all AI movement models is the availability of accurate movement data. By understanding the impacts of Social Force Theory in analysis, the certainty of a movement model can be better considered with its appropriate limitations. Such a narrative also allows for future models to be placed in context of the need for input data in general. Motivated by Lewin’s field theory study (1951), Helbing’s original social force framework (1994, 1995, re-published in 1998) places pedestrian behaviour into equations of acceleration and deceleration as a reaction to perceived information received from their environment surrounding them. The vector quantity is a representation of the social force based on temporal changes and preferred velocities of pedestrians. The original model was not intended for complex scenarios but standard ones (non-emergency for example), where Helbing and Molnar proposed that a more probabilistic approach be taken to represent those more unusual conditions. This is similar to the approach put forward near the same time within Canter et al. (1990), where these authors proposed the conception of decomposition diagrams. These were based on the probability and risk analysis that a behavioral action would occur, and a probable action based on a cumulative frequency analysis done prior of similar event-specific scenarios. Helbing and Molnar do not refer to Canter in their writings

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Fig. 4.1 Key references in the historical narrative of social force theory

as Canter is not the only theorist of behavioral frameworks at that time. There are many seemingly abandoned but rational ideas from this era that seem to have been left alone due to limitations in computational power to realize them during the late 1980s and early 1990s. Subsequently, there is modern interest to explore these as alternatives to the currently relied on movement models. Conceptually the original Helbing-Molnar framework intends, not assumes, the pedestrian travels the most comforted and shortest path to a ‘gate’ or rather area. A ‘relaxation’ is considered in speed when the agent passes or approaches an object— they are repelled by a ‘social force’. Distances from borders of objects (such as walls) are also assumed which create this repulsive effect. The motion is influenced also by other agents. The private sphere of ‘territoriality’1 is assumed resulting in these repulsive effects to the agent. They utilize a context called a sphere, which represents the area around the agent where they may be influenced by ‘forces’. It is assumed the sphere is elliptical, favoring the direction of motion though they would later interrogate this assumption and consider whether a circle of influence was more representative. Computationally, this was shown to be less demanding in later studies and adopted by Helbing and his research associates (see below). Attractive forces are assumed (people, displays etc.) which act the opposite of repulsive forces. The conception here was to allow for group behaviour. Helbing and Molnar do conceive that forces behind the agent can also be considered which would decelerate the agent, but it would have less weight away from the line of sight of the pedestrian. Helbing and Molnar finish their framework to acknowledge that fluctuations can occur for effects not perceived in general—these would be considered in future iterations of 1

A psychological terminology used in the 1990s to describe protecting one’s space. Today "territoriality" in psychology is defined by the terminology—“ownership”.

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Fig. 4.2 Social force movement theory

the framework. They view this as random or deliberate deviations akin to telling the agent what they have to do to represent complicated scenarios such as emergency egress. Motion is described between actual and preferred velocity and limited to a maximum acceptable speed. Helbing and Molnar originally coded a model of the behaviour for a conceptual demonstration through a corridor and doorway. They negated attractive (where the agent is drawn to an object) and fluctuations in that conception application. Figure 4.2 describes the social force theory. Helbing noted that the framework needed to be refined to describe opinion formation, group dynamics or other social influences noting that an abstract behavioral space would be required to be introduced. He noted that the agents would not be strategic and are more automatic in movement. Helbing et al. (2000) aimed to investigate mechanisms of what he viewed as Panic. It is critical to note that many researchers were actively attempting to dispel this terminology at that point in time. Proulx (1993) for example offered explanations for debunking the myth of Panic through stress models to explain behaviour. She and Rita Fahy later relied on notable case studies in the early 2000s to illustrate this de-bunking position (Fahy and Proulx 2009). Therefore, many contemporary behavioral theories and concepts are not present in Helbing’s 2000 framework, and there is reliance on flawed terminology. There are significant theoretical flaws to how people behave in emergency conditions which would be acknowledged in Helbing’s later writings. His efforts were to address the building on the inclusion of considering fluctuations in the framework (1998). In a former study by Helbing (1999), Helbing considered particulates, not agents, to demonstrate the concept of fluctuations which could inhibit or slow motion of a particle. Helbing extends that model in 2000 to pedestrian behaviour. Helbing goes through nine different behavioral traits

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examined but attempts to represent a collective phenomenon and build on the generalized framework discussed above. He introduces additional physical forces to the pedestrians when they begin to “touch” one another as “body” forces (to counteract compression) and sliding friction forces. These serve to impede relative tangential motion. Helbing noted that there was not an abundance of data to test the conceptual relations that he developed in order to validate the framework. Instead, he simulated instances where clogging and jamming could occur in emergency egress paths. This was done through examination of a narrow gate assuming soccer fans of representative anatomical relations, and a corridor with a large space in the center to consider a scenario where the entrance becomes jammed to oncoming pedestrians. Helbing et al. (2002) now describes panic as “irrational behaviour”, where normal behaviour is “rational” which begins to align to the contemporary debunk of the terminology called panic. His 2002 study more generally aligns with contemporary behavioral theories discussed in the fire and emergency-based research fields. In this consideration, the pedestrian is now controlled with an on–off parameter called nervousness which influences herding effects, fluctuations, and speed inputs into the model. Helbing separates the force models as normal pedestrian dynamics and stressed pedestrians; he switches these behavioral models on or off. The work presented is largely the same as 2000’s framework but with additional revised terminologies to reflect more contemporary behavioral research, i.e., that rational behaviour can occur in emergency conditions. There are various discrepancies to proper planning procedures illustrated in the 2002 presentation of the framework. For example, consider the introduction of placing obstacles in front of exits to dissipate the flows through exits based on modelling results; While computationally gains are shown, in reality, significant issues can arise with real behaviour. The 2002 study ends by also noting the need to include fire and smoke propagation modules and behavioral reactions to these effects again noting that data is not available to do so. There is acknowledgement through the study to other evacuation tools such as EXODUS. Johansson et al. (2008) gave recognition to other behavioral movement frameworks such as cellular automata. This is an artificial life approach according to Blue and Adler (2001) entities (automata) occupy cells and are governed by local behaviour rules that are meant to approximate reality. This is considered to be a frequent alternative to the social force model (Chen et al. 2018); particle hopping, and multi agent approaches in addition to the Social Force Theory framework. In general, all the movement models needed calibration for data to validate their models. They acknowledge that the social force framework has not been calibrated well to evacuation and proper dimensioning of pedestrian decision space. Helbing and associates optimize the social force parameters based on trajectory gained from video tracking. The focus on the angular dependence to improve their calibration (angle of the pedestrian movement to their normalized distance). They acknowledge a distance dependence on the basis of a circular specification (a function of the strength of interaction, interaction range, and size of pedestrians). Their approach was a hybrid simulation of tracked pedestrian movements to virtual pedestrians to assess the fitness of parameter sets used in the social force model (minimizing the error). They tracked people moving off an escalator. The analysis did not consider atypical behaviour of

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real pedestrians, they were removed from the calibration. Repulsive potential was also calibrated as either in the form of an ellipse and circle directed into the direction of motion with an ellipse found to characterize a better fit. Helbing also found that by performing this study people were only influenced by what happened inside a 180-degree angle in front of them (their visually perceived area). These authors (Johansson 2008) discuss assumptions made to the model to enable large scale analysis. This was by truncating influence distance to 5 m in front and assuming a circular influence on speed computational time. They discuss their software called “UNIVERSE” which is tailored to include: pathfinding/route choice modules; spontaneous stops; calibration modules to adapt simulations to real measurements; statistical modules (densities, flow); visualization modules; and functionality to reflect density-dependent herding effects (which their work is vague on defining how). Helbing’s study points that calibrations will need to consider age distribution handicap individuals, those carrying luggage, alcohol consumption, cultural habits, use of mobile phones, and situational contexts (religious activities for example) for proper calibrations and results are not readily applicable to be cross applied to every situation. These authors would later propose a group forces model for the arrangement of agents travelling in small groups up to four people. Social forces were used to attract agents into groups and arrange them to match observations. Parameters were defined to affect the behaviour of agents in groups. Calibration of these parameters was done. Later literature would show various authors exploring calibrations of the social force framework with focus on pedestrian trajectories (Wolinski et al. 2014), density distributions (Zhong et al. 2015) and body/ sliding calibration in total evacuation (Li et al. 2015). These contributions did not influence the efforts of Helbing and associates. Moussaid et al. (2011), acknowledge that the social force framework reproduces movement observations quite well but goes on to list various limitations. The first limitation is that it is difficult to capture the range of crowd behaviours in a single model where extensions to interaction functions described above in previous subsections required extensive sophisticated mathematical expressions that are hard to calibrate. In their 2011 study, they cite Moussaid (2009), where they performed controlled experiments that revealed mechanisms and functional dependencies of pedestrian interactions. Contrary to the previous work they did not use a prefabricated interaction function and fitted parameters to the data, they extracted dependencies between certain variables from the data and identified mathematical functions fitting them. That work highlights cultural bias in the way people make decisions particularly how they pass an individual they may collide with (right side versus left side). The second limitation is that the model is based on binary interactions (one person versus a group of people would fall outside the scope) and how this can be adequately accounted for via an average, summation etc. Helbing and his associates move away to a new approach where they consider a cognitive science approach based upon behavioral heuristics to overcome what was identified as the problems above. Their model now seeks a free path in movement as opposed to repelling against neighbors. They create a two-step framework that breaks down to solve what information is used by the pedestrian and how is this information adapted to control walking

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behaviour. They focus on vision as the primary basis of movement and based on visual information how it is used to determine walking speed and direction. They assume a reaction time in decision making to be half a second as seen in their previous studies (2009). Many assumptions of the social force framework developed earlier are carried forward in this new framework. For example, the representations of an individual by virtue of a circle projection on a horizontal plane. Horizontal distance is assumed to be the time of first collision to an object by assessing all objects in visual front a default maximum is assumed. Cognitive heuristics2 are considered by acknowledging first that a pedestrian chooses a direction that allows the most direct path to a destination point while considering the presence of obstacles. The chosen direction is done by minimizing the distance to the destination function (a function of angle of walking choice, maximum distance, and distance to first collision). The desired walking speed is chosen by maintaining a distance from the first obstacle in a chosen walking direction that assumes the time of collision is at least high enough to stop in case of an obstacle. They also extend the framework to account for physical contact forces (either intentional to perceived visual cues, or unintentional due to collision). They give equations for this motion. Various simplified analysis simulations were then considered for uni and bidirectional flows with large and small densities. Crowd turbulence and stop and go wave phenomena were observed with these simulations therefore the authors felt their simulations had credence. At this stage (Helbing and Johansson 2011) Helbing and associates describe that eye tracking technologies could yield more accurate advances and explore the adaption of these algorithms to automation and mobile robots. They discuss how this model would inspire new collective human behaviour models to follow (group behaviour andsocial activities). Helbing and associates (see Moussaid et al. 2016) continue studies on crowd behaviour moving towards virtual reality analysis for data collection and away from conceptual model application relating to social forces. State of the art reviews are in rarity, though recent ones were explored by Duives et al. (2013) and more recently Chen et al. (2018) which detail other external efforts beyond Helbing to develop these and other pedestrian frameworks. Herein, the pedestrian modelling simulation software Massmotion is considered for the modelling of the stadium egress. The authors utilize this software as well as it can be adaptive to developmental changes through development kits for exploratory and research purposes. For example, the concept of improving group forces between agents in the models can be explored as considered by Young (2021). Figure 4.3 describes this process in improving upon group behaviors. This technique is not used but worth remarking as future research in adapting other assisted behaviors in the model space to more realistically represent people requiring assistance in 2

Cognitive heuristics are mental shortcuts that people use to simplify tasks. They are like rules of thumb used to make decisions quickly without expending significant effort to be accurate. A good example is guessing or rounding when doing complicated calculations, like estimating 69 × 99.5 to approximately 69 × 100.

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Fig. 4.3 Group forces through software development kits

movement which would be seen with some disabled persons populations. It should be noted that when using development kits, a verification and validation process follow before these are used in commercial or research projects.

4.3 Evacuation Model Generation and Limiting Assumptions The stadium representation in the model was constructed to scale in AutoCAD using as built design blueprints provided to the authors by the stadium officials. The file was then imported as a CAD file into the MassMotion model space (MassMotion 2021, Version 10.6). See Figs. 4.4 and 4.5. This pedestrian movement software herein allows the user to specify parameters to simulate the desired movement scenario and populate the space with a desired population. The author’s jurisdiction predominately utilizes this software, MassMotion, for design (e.g. Smith 2022). This software was also chosen as it is used by the industrial collaborator, and because it is one of the most globally used programs (Lovreglio et al. 2020). The software is under constant improvement and development. The current version at the time of writing this book is Version 11. Alternative pedestrian movement software can be used for the same illustrative effect demonstrated herein. Comparisons by the authorship team of different pedestrain movement software have shown similar results in terms of evacuation time (Gales et al. 2022).

4.3 Evacuation Model Generation and Limiting Assumptions

Fig. 4.4 Stadium drawing to model space

Fig. 4.5 Stadium MassMotion model space

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4 Evacuation and Pedestrian Modelling in Stadia

Profiles are used to define agent characteristics for use in the model. Parameters are largely dependent on the walking speed in meters per second (minimum, maximum, mean, and standard deviation), but other adjustments that affect agent dimensions and route choice algorithms can also be made if necessary. All models simulate lowmotivation scenarios, meaning they are not representative of emergency evacuations. Pre-movement times were not implemented as the goal is to investigate the influence of speed difference with varied demographic scenarios. This enabled the authors to compare the models based on varying profile parameters and demographics distributions to isolate the impact of project-specific data to overcome limitations of industry standard metrics and increase certainty, particularly involving vulnerable populations with mobility related disabilities. These models serve to analyze the limitations of current modelling methods and highlight the importance of collecting and inputting more detailed data for practitioner use. Note that these scenarios are presented as an illustration of the impact on project-specific data and are not meant for general design purposes. They are illustrative in nature.

4.4 Evacuation Model Scenarios and Description Four comparative crowd simulation scenarios were considered to analyze the impact of project-specific data which considers accessibility aspects as opposed to relying on homogenous standard movement metrics. The following scenarios were modelled; they vary demographic distribution and movement profiles: Scenario 1: Current Default Parameters (Fruin movement speeds). Scenario 2: Manual Input Parameters for Average Population (Tables 3.2 and 3.3, both not inclusive of complex profiles). Scenario 3: Manual Input Parameters for Observed Population (Tables 3.2, 3.3, 3.5, 3.6 and 3.7). Scenario 4: Manual Input Parameters for Forecasted Population (Tables 3.2, 3.3, 3.5, 3.6 and 3.7). Note in all scenarios, the default MassMotion assumption for percentage of natural speed was assumed for stair use. This is a weighted factor based upon stair inclination (Oasys 2019). This was followed as Tables 3.4 and 3.8 were non inclusive of all demographic types used in scenarios due to data collection limitations noted in Chap. 3. It should be noted that Tables 3.4 and 3.8 may give more conservative reductions in the calculation of stair speed movement reduction in comparison to potential default parameters in software or user assumption. The profile radius is defined as half the distance from shoulder to shoulder (Oasys 2019). The default radius is 0.25 m, and within the MassMotion manual, it is advised that any modifications to these parameters be within a range of 0.15–0.40 m (Oasys 2019). Data acquisition for radius parameters of these profiles derived herein have not yet been established, nor are of accessible use to the authors. Note that in preliminary and

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65

exploratory modelling performed by the York University fire research team considered the effects of changing radii for certain populations using an earlier version of MassMotion (see Ferri et al. 2020), however based upon the camera angle in the data collection used in Chap. 3, the exact radii were not possible to quantify with appropriate consideration to variability, and hence the authors only use the default value herein. Therefore, the default radius 0.25 m was used for all the agent profiles for the purpose of this study. Profile radius should be re-examined in future anthropometry research and effects in modelling considered. This implies that the differences seen in the default model and the authors’ scenarios will be on the non-conservative side for comparison as the higher radii will lead to congestion and blockage in the stadium. In addition, the total population was set to 6316 people for one round of each scenario and then 2100 for a second round of the same scenario. A population of 6316 is representative of the maximum population in the lower stadium bowl. The lower population of 2100 represents a typical population in early matches in the tennis tournament. With the exception of Scenario 1—the Current Default Parameters—the constructed models have adopted the agent profiles developed in Chap. 3. Scenarios 2, 3 and 4 vary only by demographic distribution, meaning the characterized proportions of each population that is inputted to the software, which is described in further detail proceeding Table 4.1. The data can also be further subdivided by age groups; however, some data sets lose their statistical significance when this is done. Scenario 1 illustrates current modelling applications that rely solely on industry standard default profiles. It is the simplest simulation of the composed scenarios. Its limitations include that it does not include any project-specific data on the population; therefore, no data is manually inputted for demographic distributions, speed parameters, nor radii. Instead, it uses the outdated, pre-set default parameters (the Fruin commuter profile and distribution) of the software. It would be considered to have the highest uncertainty regarding parameter. Scenario 2 uses manual input parameters for the average population. It is similar to Scenario 1 in which it does not consider the vast diversity of movement profiles and instead limits movement representation to that of non-disabled profiles. However, the scenario has higher certainty as it is built using the profile parameters and demographic distributions that were observed at the stadium event, as opposed to relying on default standard metrics such as a Fruin movement profile. Scenario 3 uses manual input parameters, but instead of using the average population in Scenarios 1 and 2 the observed population was used. This scenario would have the highest certainty in respect to input movement parameters used because it was configured to reflect the observed population at the event as accurately as possible. It improves Scenarios 1 and 2 by including a diverse set of profiles, not limited by distribution curves nor average populations. In addition to the non-disabled profiles, new profiles were created with custom parameters for mobility-related disability persons (e.g., using cane, crutches, persons requiring assistance, or walking stick), persons living with obesity, (young adults, adults, and elderly), and other reduced mobility cases (oversize luggage). The demographic distributions were assigned based on the

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Table 4.1 Demographic distributions for each scenario where percentage is multiplied by total population and profiles link to those presented in Chap. 3 Scenario 1

Scenario 2

Scenario 3

Scenario 4

“Default”

“Average”

“Observed”

“Forecasted”

Fruin commuter (%)

100.00

0.00

0.00

0.00

Total (%)

100.00

0.00

0.00

0.00

Default movement profiles

Non-disabled movement profiles Child (%)

0.00

15.00

15.00

14.00

Young adult (%)

0.00

25.00

15.00

12.00

Adult (%)

0.00

35.00

35.00

11.00

Older persons (%)

0.00

25.00

10.00

3.00

Total

0.00

100.00

65.00

40.00

Disabled movement profiles related to mobility Cane (%)

0.00

0.00

0.06

2.82

Crutches (%)

0.00

0.00

0.01

0.47

Req. assist (%)

0.00

0.00

0.09

4.19

Walking stick (%)

0.00

0.00

0.03

1.40

Total (%)

0.00

0.00

0.19

8.87

People living with obesity movement profiles Adult (%)

0.00

0.00

22.58

34.95

Older persons (%)

0.00

0.00

11.00

14.95

Total (%)

0.00

0.00

33.58

49.90

Other reduced mobility movement profiles Oversize luggage (%)

0.00

0.00

1.23

1.23

Total (%)

0.00

0.00

1.23

1.23

Combined (%)

100

100

100

100

population proportions that were observed at the stadium event. The observed distributions of the more complex profiles were assigned first, and then these proportions were subtracted from the respective non-disabled profiles according to age. Scenario 4 was constructed to give insight to inclusive universal design in the future and is exploratory in nature. The profiles used are the same as seen in Scenario 3. As an extension of this scenario, the demographic distributions are not reflective of the observed population at the stadium event but are rather defined by a variety of national demographic statistics provided by Statistics Canada (2019), Gales et al. (2020). Like Scenario 3, the demographic distributions were first assigned to the more complex profiles based on their prevalence in the population, and then subtracted from the respective non-disabled profiles. By aligning the crowd demographics with those of the Canadian population, this theoretical crowd simulates an ideal diversity that inclusive designs aim to achieve. Future scenarios could also be performed for

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67

interest where data is available. This could also reflect aging populations, but this is beyond the scope of this illustrative example and would require additional data collection of demographic ticket sales of those attending the tennis tournament.

4.5 Evacuation Model Results Using the conventional crowd simulation software, each simulation was run ten times with the distribution of agents randomized each time. The number of people egressed with time was recorded. While the study herein did not follow the procedure outlined by Smedberg et al., where a multifactor variance approach was used to determine the number of simulations (Smedberg et al. 2021), negligible differences were observed between each simulation. In future research, more modelling runs can be completed to lower model uncertainty, but for the scope of the study herein, this was deemed unnecessary as the scenarios are meant to be illustrative in nature as opposed to being used for an actual commercial design. The mean results were calculated for each model and tabulated in Table 4.2 via percentage difference from Scenario 1 with time. Note the value for the 6316 population simulations was calculated using the percentage from the total of 6316 while the value for the 2100 population was calculated using a total of 2100. The deviations observed in all scenarios were with respect to total evacuation time with egressed population. Deviation is expressed as a percentage (deviation in time of the ten runs for evacuation number divided by its average evacuation time in seconds). Deviations were 1.0, 1.6, 1.5, and 2.2% respectively for Scenarios 1 through 4 where n = 2100 and < 1% for Scenarios 1 through 4 where n = 6316. Table 4.2 Time for mean percent population to egress for all models % of population egressed

Time of egress (s)

Percentage difference from scenario 1

Population = 6316

Population = 6316

Population = 2100

Scenario 5

Population = 2100

Scenario

1

1

2

3

4

54

35

1.9

1.9

1.9

2 2.9

3 2.9

4 0.0

15

90

55

0.0

1.1

2.2

0.0

1.8

0.0

30

130

69

1.5

2.3

3.8

8.7

8.7

10.1

45

167

92

1.2

1.8

4.0

0.0

1.1

2.2

60

202

110

1.5

2.0

3.0

0.9

1.8

4.6

75

239

129

1.7

2.5

4.2

3.1

3.9

6.2

90

285

154

2.8

3.5

5.3

5.8

6.4

10.4

95

306

165

Average

2.9

3.6

5.9

11.5

12.7

18.2

1.7

2.3

3.7

4.1

4.9

6.5

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The larger deviations observed with smaller population size are due to the egress model exhibiting unimpeded flow where variabilities in the input speeds can be seen and faster agents can have the ability to overtake slower moving agents. Upon fuller capacity the crowd density begins to have an effect and the egress model exhibits a closer range of speeds as congestion governs (led by the slower agents). Each scenario run therefore gives a similar low deviation result with higher capacities in the stadium. Scenarios which featured higher proportions with slower movement speeds were observed to have higher deviation. As the egress model randomly places these agents between each simulation run these slower moving populations led to larger differences between each simulation run. This has beneficial implications to considering modelling where specific seating may be provided to optimize egress. This however is beyond the scope of this current research presented in this chapter.

4.6 Analysis and Discussion As seen in Table 4.2, the initial egress of Scenario 1 is about the same or slightly slower than the egress of the other three simulations. This may be caused by the high density that appears at the start of all four simulations. As aforementioned in earlier chapters, the speed of the crowd is largely controlled by the density. However, as anticipated, the overall time for egress increases as the demographic distributions are increasingly specified with each model. This is mainly due to the increasing proportions of profiles with reduced speeds. In general, as the population is more congested in the stadium, this tends to govern the movement as opposed to when the occupancy is much smaller (1/3 occupancy). This is an expected trend given that in the latter condition congestion is less as likely as the population is smaller and there is more space for the modelling differences to be observed based on speeds. When comparing Scenario 1 and Scenario 2, the maximum percent difference in the data for the populations 6316 and 2100 was 2.9% and 11.5% respectively, while the average differences are 1.7 and 4.1%. The lower percent differences are because the mean speeds for non-disabled profiles are within the range of the default parameters, resulting in a difference in egress time and observed behaviors. The crowd is showing a “short-board effect” in evacuation, as agents with the slowest speed tend to govern the average moving speed of followed agents in narrow pathways such as stairs. It should be noted that the non-disabled movement profile is indicative of a diverse range in speeds among each demographic. According to the non-disabled profiles, the minimum speed of agent population (child and older person) in Scenario 2 is half of the default minimum speed used in Scenario 1. With a higher standard deviation, a slow agent is more likely to be generated under Scenario 2. It explains the reason that even with the profile mean speed being similar, the percent difference between Scenario 1 and Scenario 2 is obvious. While Scenario 3 shows a slower egress than Scenarios 1 and 2, it is very similar to the egress seen in Scenario 2. The maximum percent difference between Scenarios 1 and 3 is 3.6% for the simulation

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where the population is 6136 and 12.7% for the 2100 population simulation. While the average differences are 2.3 and 4.9%. Scenario 4 shows the slowest overall egress, as the proportion of more complex profiles with slower speeds increases drastically in comparison to the previous models. The average percent difference increases to 3.7 and 6.5% for the two simulations when compared to Scenario 1. This shows that with a higher population of disabled persons, the crowd movement is affected more by the speeds of the pedestrians. With more individuals moving at a slower rate, the time of aisles and stairways retaining maximum density increases and the overall egress slows. This model highlights interesting points in future stadium design. To accommodate the vast population of persons with accessibility needs in Canada in the design of stadia will necessitate a range of demographic scenarios to be considered. These calculations show how using the default parameters in Scenario 1 can severely underrate a required egress time. Although Scenario 1 shows strong results in comparison to the described distribution of average profiles in Scenario 2, this is not reflective of the many different movement capabilities observed in the present crowd. Scenarios 1 and 2 fail to acknowledge the more diverse portions of the population. The third simulation shows that a small decrease in egress times will be caused by implementing disabled individuals. However, with such a small population of these individuals, the impact is not significantly large. Overall, the current scenarios (1 and 2) are not reliable methods for simulating the demographic distributions seen in Canada’s population in Scenario 4. It is important to note that although this study presents ways to overcome some limitations of default crowd modelling assumptions, additional research is still required to further develop the pedestrian movement tools. Most notably, the presence of complex profiles is limited to those presented in Scenarios 3 and 4, whereas there are many more profiles that would impact the functions and outputs of these models. This includes other disabled people that may be a result of psychological and physiological variation such as neurodiversity, mental health conditions, sensory conditions (sight/hearing), etc., and other movement behaviors that result from intoxication, cellular mobile usage, etc. This limitation is in part due to the lack of available movement data for the vast variety of profiles, and the inability of crowd simulation software to accurately incorporate said demographics. In addition to this, the crowd simulations presented in this study are defined by the architectural features of this stadium, meaning that these results and the impacts of relying on industry standard metrics would likely vary for a different environment. More specifically, as this stadium consists of relatively short travel distances in comparison to larger stadia, the effects of fatigue are not incorporated. Therefore, in other cases that the user is required to increase the travel distance, the size of the crowd, and/or the proportion of more complex profiles, the authors believe these will all contribute to increased egress times and different trends in human behavior.

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References Blue, V., and Adler, J., 2001. Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Res. B 35:293–312. Canter, David , (Ed.), 1990. Fires and Human Behavior, David Fulton. Chen X., Treiber, M., Kanagaraj V., Li, H. 2018. Social force models for pedestrian traffic—state of the art. Transport reviews. https://doi.org/10.1080/01441647.2017.1396265. Chin, K., Young, T., Chorlton, B., Aucoin, D., and Gales, J. 2022a. Crowd Behaviour in Canadian Football Stadia—Part 1—Data Collection. Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7). Chin, K., Young, T., Chorlton, B., Aucoin, D., and Gales, J.2022b. Crowd Behaviour in Canadian Football Stadia—Part 2—Modelling Canadian Journal of Civil Engineering (Canadian Science Publishing). 49(7). Duives, D. C., Daamen, W., and Hoogendoorn, S. P. 2013. State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies, 37, 193–209. Fahy, R. F., and Proulx, G. 2009. ‘Panic’ and human behaviour in fire. Proceedings of the 4th International Symposium on Human Behaviour in Fire: 13 July 2009, Robinson College, Cambridge, UK, pp. 387–398. Ferri, J., Young, T., and Gales, J. 2020. Authenticating Crowd Models for Stadium Design. 5th FEMTC8 pp. Gales, J., Champagne, C., Harun, G., Carton, H., Kinsey, M. 2022. Fire Evacuation and Exit Design in Heritage Cultural Centres. Springer Briefs in Architecture and Technology (Springer-Nature). 5 Chapters, 75 pp. Gales J, et al. 2020. Anthropometric data and movement speeds. SFPE final report. Helbing, D. 1994. A mathematical model for the behavior of individuals in a social field, Journal of Mathematical Sociology 19 (3), 189–219. Helbing, D., Farkas, I., and Vicsek, T. 2000. Simulating dynamical features of escape panic. Nature, 407, 487–490. Helbing, D., Farkas, J., Molnar, P., Tamas, V. 2002. Simulation of Pedestrian Crowds in Normal and Evacuation Situations. Pedestrian and Evacuation Dynamics, Publisher: Springer, Editors: Schreckenberg, M. and Sharma, S. D., pp. 21–58. Helbing, D., and Johansson, A. 2011. Pedestrian, crowd and evacuation dynamics. New York: Springer. Helbing, D., and Molnar, P. 1995/1998. Social force model for pedestrian dynamics. Physical Review E, 51, 4282. Helbing, D., Farkas, I. J., and Vicsek, T. 1999. Freezing by heating in a driven mesoscopic system. Physical Review Letters, 84, 1240. Johansson, A., Helbing, D., and Shukla, P. K. 2008. Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems, 10, 271–288. Lewin, K. 1951 (ed.). Field Theory in Social Science, Harper and Brothers, New York. (No digital copy available). Li, M., Zhao, Y., He, L., Chen, W., and Xu, X. 2015. The parameter calibration and optimization of social force model for the real-life 2013 ya’an earthquake evacuation in China. Safety Science, 79, 243–253. Lovreglio, R., Ronchi, E., Kinsey, MJ. 2020).An Online Survey of Pedestrian Evacuation Model Usage and Users. Fire Technol 56:1133–1153. https://doi.org/10.1007/s10694-019-00923-8. Moussaid M, Helbing D, Garnier S, Johansson A, Combe M, and Theraulaz G. 2009. Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc. R. Soc. B 276, 2755–2762. Moussaid M, Helbing D, Theraulaz G. 2011 How simple rules determine pedestrian behavior and crowd disasters. Proc. Natl Acad. Sci. USA 108, 6884–6888.

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Moussaid M, Kapadia M, Thrash T, Sumner RW, Gross M, Helbing D, Holscher C. 2016. Crowd behaviour during high-stress evacuations in an immersive virtual environment. J. R. Soc. Interface 13:20160414. Oasys. 2019. MassMotion Help Guide. London Proulx, G. 1993. A stress model for people facing a fire. Journal of Environmental Psychology, 13, pp. 137–147. Statistics Canada. 2019. Overweight and obese adults, 2018. In: Government of Canada. www.sta tcan.gc.ca. Accessed 20 May 2021. Smedberg, E., Kinsey, M., Ronchi, E. 2021. Multifactor Variance Assessment for Determining the Number of Repeat Simulation Runs in Evacuation Modelling. Fire Technol Smith, M. 2022. Pedestrian Modelling for Resilient Structures. https://www.entuitive.com/ensightspotlight-home/pedestrian-modelling-for-resilient-structures/. Accessed 3/1/2023. SFPE. 2019. SFPE Guide to Human Behaviour, Springer, 2d Ed. 2017. Young, T. 2021. Semiautomated Analysis of Pedestrian Behaviour and Motion for Microsimulation of Transportation Terminals. Graduate Dissertation, York University. Wolinski, D., Guy, S. J., Olivier, A. H., Lin, M., and Manocha, D. 2014. Parameter estimation and comparative evaluation of crowd simulations. Computer Graphics Forum, 33, 303–312. Zhong, J., Hu, N., Cai, W., Lees, M., and Luo, L. 2015. Density-based evolutionary framework for crowd model calibration. Journal of Computational Science, 6, 11–22.

Chapter 5

Strategies and Technology for Effective Evacuation Design of Stadia

Abstract This chapter discusses strategies and best practices to improve the evacuation performance of stadia. Considerations for accessibility and movement of different demographics are reviewed, including the impact of future population and demographic trends. The evolution of methodologies for demographics-based analysis of pedestrian movement and behaviour are discussed, incorporating AI-based analysis of large datasets. Modifications of open-source software for pedestrian analysis are presented. Evacuation and circulation simulations using data generated by the occupants inside the building can be used to model travel and egress times more accurately. Ultimately, these technologies can be combined to perform real-time occupancy tracking and evacuation modelling using a digital twin of the site, determining the best evacuation routes, and directing evacuees accordingly in the event of an emergency.

5.1 Strategies for Effective Evacuation Design of Stadia This book worked towards allowing designers to better understand the variety of reduced mobility cases inclusive of mobility related disabilities and what environmental factors hinder accessibility and usability. This study herein, recognized the lack of people movement data particularly for stadia design. The phased research procedure developed an understanding of the current demographics of mobility related disabled persons in stadia; quantified the available speeds of mobility related disabled persons on stadia grounds; and lastly developed an understanding of what design impact (for function) a growth in population of mobility related disabled persons to the stadium will have in the future overall design of the evacuation process. This book will contribute towards steering future research towards this goal of preparing stadia for future populations through accessible design. The first phase of this study used a survey to analyze and explore possible trends and influences on participation. Implementing accessible features significantly attracts persons with mobility requirements, but it is just as important to advertise © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Gales et al., Egress Modelling of Pedestrians for the Design of Contemporary Stadia, Digital Innovations in Architecture, Engineering and Construction, https://doi.org/10.1007/978-3-031-33472-6_5

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and promote these features for public awareness. The survey allowed direct communication and feedback from stadium users. The survey’s key conclusions indicated: the lack of attendance from those of reduced mobility can be a result of lack of awareness by attendees and staff; most accessible changes are relatively attainable from a design standpoint (effective signage, indicators of elevators and maps, shading, seating); that the governing populations of reduced mobility cases exist significantly within the adult population; and that public participation of reduced mobility persons may have a relationship to socioeconomic trends and their attendance at higher priced events (finals for example) may be sparser. The survey proved to be effective in identifying barriers and suggesting ways of improvement. 19% of the interviewees were unaware of accessibility features, which confirmed the importance of awareness in accessible features seeing as a lack thereof can completely deter the population. The second phase of research, involving the observational study using film recordings of the stadia, revealed population proportions of 0.31% for the attendance of mobility related disabled persons. In comparison to the national statistics that 9.6% of Canadians aged 15 years and older report one or more mobility related disabilities (Morris et al. 2018), a clear and definite lack of public participation is identified. This suggests that there are challenges to overcome in the current system that disrupt the social involvement of disabled populations. The lack of attendance suggests the need for engineering investigation and proposals to incorporate accessible design to promote the inclusion of disabled people. It also leads the authors to believe that socio-economic factors as well as crowd sizes have an impact on the willingness of disabled persons from attending stadia events and therefore must be considered when designing for inclusion. The survey correlated to these aspects to act as a baseline for the types of changes that can be implemented. The observational study also demonstrated that the adult population appear to move at the fastest average speed on level ground, whereas the older persons demographic moves at the slowest average speed. Those using a cane, crutches, walking stick or a person requiring assistance moved at a slower rate than the others. The common factor separating the slower demographic from the faster demographic is that those who move faster generally use an assistive movement device that has wheels, while the slower do not. When comparing the data for people living with obesity versus individuals without obesity on level ground, the speed of those visibly living with obesity is slower than those who are not. ‘Any’ alcohol consumption does have an impact on an individual’s walking speed, making them walk slower than normal. Overall, those who are visibly living with obesity, or have consumed alcohol move in similar speeds to disabled persons. Upon further investigation, an additional percent of the population was identified as experiencing some form of reduced mobility not caused by a visible mobility-related disability. The proportion was found to be 3.15%. The results show a diversity of disabilities present that affect more than just mobility-related disabled persons. In fact, it is believed to affect an even greater population that was not attainable within the parameters of this study. Specifically, those with invisible mobility related disabilities such as emotional, sensory, learning and cognition requirements as well as non-disability items that may affect movement such as gender, age and culture were not holistically included in the study. It is important to understand their movement behaviour as well

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as their movement profile. Collecting this data allows for it to be placed in pedestrian evacuation models and thus allows designers to consider their needs in the design of future structures. The datasets and observations derived from the first two phases of research enabled the authors to illustrate the effect of modifying egress model parameters to reflect insitu environment conditions. The preliminary models in the study showed promising results and reliability for future modelling methods even where the AI may be refined. The findings on the amount of people egressed with time highlight the limitations of using Fruin’s outdated (1971a, b), single profile distribution, in comparison to project-specific details on observed populations. Crowds were observed to show a “short-board effect” in evacuation, as agents with the slowest speed tend to govern the average moving speed of followed agents in narrow pathways such as stairs. Scenario 4, with the most diversified populations moving at slower rates illustrated that aisles and stairways retained maximum density slowing overall egress and indicating that interest points in future stadium design (such as aisle width and stairs) need consideration to better accommodate the vast population of persons with accessibility needs. Identifying and removing these barriers will thus create a universal design that is equally inclusive of individual abilities and regards their safety. This will also help establish legislation for Canada, as recognized by the Accessible Canada Act and strengthen it internationally. The findings herein will aid in the growth and prosperity of businesses by increasing their opportunity to offer services to a greater range of the population. All phases of research pointed to critical research needs. This study brings attention to the prevalence of varying movement abilities and how excluding them can lead to inaccurate evacuation results. Instead, profiles must be included in modelling methods to accurately depict the population at hand, and work towards creating inclusive designs. As being considered by the authors, the stadium profiles and model will require validation against observed egress for additional confidence (Gwynne et al. 2017). This should be performed in conjunction with observations seen in reference (Young et al. 2021) over a range of software. Future studies should also explore the construction of fundamental diagrams based on population densities observed in the stadia. Modelling software herein is currently calibrated to reflect a decrease in speed using unimpeded speeds. These relationships should also be explored in the future. The production of these will require new camera angles and technologies to capture appropriate data. It is critical that future research considers the data collection to incorporate disabilities in modelling that are a result of psychological and physiological variation such as neurodiversity, mental health conditions, sensory requirements (sight/hearing), etc., and other movement behaviors that result from intoxication, cellular mobile usage, etc. Understanding human behavior and factors is a complex process that requires extensive studies beyond the parameters of this first stage study, and universal design requires this unique data in their development and application to ensure inclusivity and safety. One of the key factors in understanding human behavior for design purposes is walking speeds of various profiles subjected to various environmental

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and situational conditions which require extensive research. In the future, a range of crowd modelling software typically used in stadium design should be considered. In addition, future studies will expand upon the datasets to include profiles of those with psychological variety, other physiological variety, and mobile usage, to name a few. This can be done from continued surveys, interviews and questioners for direct feedback. Therefore, to work towards producing universal designs, continuing studies are required to improve our understanding of crowd demographics for various environments, the behaviors of the respective individuals, the associated barriers, and the accommodations required.

5.2 Technology for Effective Evacuation Design of Stadia It must be recognized that the field study undertaken as part of this book had a high cost in reflection of human resources and equipment needed to complete it. The research presented herein began in 2018 and is only now entering a second phase with a pre and post covid mobility study at the same stadium by these authors in 2023. Subsequently, during the study time and since 2018, several technologies have emerged which can speed the generation of movement data and improve the algorithms used by pedestrian evacuation modelling software. This section highlights these by describing efforts to generate living databases to hold movement speeds and the credibility of such movement data, as well as the emergence of alternative Artificial Intelligence (AI) frameworks for evacuation modelling and collection of movement data.

5.2.1 Collation of Movement Speed Data Representing the movement of people properly has critical importance for the design of buildings and infrastructure—particularly in the case of emergency or high stress state evacuations, but also, for normal day-to-day circulation. These designs and analyses of movement in buildings and infrastructure can be considered some of the most performed design and analysis techniques a fire protection engineer may perform on a day to day working basis. Currently pedestrian models are configured using speed and anthropometric input data. When designers build and/or configure their pedestrian movement models applicable and defensible data sets are required. Reducing the level of uncertainty is the desire, as the existing data may not entirely be applicable to the designer’s scenario or even be available. The study herein discussed at length the movement characterization of difficult to study population groups such as those with mobility-related disabilities. These movement speed data sets have certain limitation as they were collected from a field study without adjoining survey that could obtain additional information about the profile collected in nature. For example, the camera view utilized in the stadium is not optimum in ascertaining

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exact anthropometrical measurements which have seen usefulness in the future of evacuation modelling (see Gales 2020). While a field study is very useful to collate movement data of large population groups, smaller lab-based studies have high use for really understanding the movement profiles that can be observed in large population groups. However, this will present ethical limitations in what can be studied and its realism. Currently existing movement datasets populate the current SFPE handbook (2015). Traditionally the data follows normalized movement speed profiles (min, max, mean, median and deviation) with specific information to subdivide to the type of study, specific data being collected, and infrastructure considered. While the presentation informative at times it can be difficult to collate without appropriate interface with the evacuation model. This technology concept was explored by the authorship team and demonstrated that institutional efforts can be undertaken to the creation of an entry portal (see Fig. 5.1 for an example of its construction). Grey text boxes represent the categories of datasets, where the green boxes represent the searchable parameters of each field. Movement data from peer reviewed sources can be collated within Comma Separated Value (CSV) tables and populated by a user through a basic interface that they can instruct. These movement speeds then can be uploaded into the main portal. The user can then filter and retrieve project specific data through a generalized query that is performed. Efforts to publicly release these forms though come with challenges in maintaining appropriate data integrity of what is submitted into the database (for example errors from source data) so such a technology isn’t publicly available, but easily constructed should the volume of work in pedestrian movement studies exist for the practitioner to invest the time into the technology at their place of work. As the source documentation improves with additional handbook data this will also be practical to store additional anthropometric data on the movement profiles. The authors recommend that if such a technology is utilized by a practitioner and constructed that care be taken to examine the source material being referenced for its certainty in quality. There are many circumstances in available literature where movement profiles are just assumed and not directly calculated and where source publication is now lost so proper analysis cannot be made by the user. The duty of the designer to understand this information is critical and this process should be undertaken before using such a technology and hence why the authors do not make it available.

5.2.2 Future of AI Technologies for Egress and Movement Modelling Previously Gales et al. (2020, 2022) highlighted several future directions required in the collation of movement data. The existing trend to record and analyze movement speeds and basic anthropometric data fits within the existing movement frameworks and the current modelling tools which currently are used in industry. However, using

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Fig. 5.1 Example of data that can create a movement database

this traditional framework of movement capture negates the complicated decisionmaking behavior of people itself which vary from building type to building type. For example, how the building is being used by someone with a mobility related disability would affect their route and movement. Figure 5.2 shows such an example of a user of the pedestrian space waiting for assistance at the stadium in sequenced imagery during three minutes of filming time. The ground pavement in the forward direction is uneven requiring the person to need assistance to enter the main grounds. Analyzing this location of the stadium then shows at least six wheel-chair users

5.2 Technology for Effective Evacuation Design of Stadia Fig. 5.2 Pavement service effecting movement behaviour of a person with a mobility-related disability

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(mobility related disability) who were challenged with this particular pavement seen on the day of filming (the pavement is now since redone and smoothed at the time of writing). If the researcher is only collecting speed data from point a to b, important observations of movement behaviour and usage of the space may be lost. Actual movement behaviour can have impact on certain assumptions made in modelling when these profiles are used. There is value to further analyze movement behaviour in recorded videos that exist for previous studies. Current movement models have commercial and research value, but they ultimately are based on very simplified movement algorithm rules and limited data sets for validation as described in Chap. 4. A number of these movement models and frameworks were developed in the 1980s–1990s when ASET/RSET (actual required safe egress time to required safe egress time) was popularized and when computers were limited in processing power and unable to represent complicated movement behavior. The creation of revised movement AI and rule-based probabilistic behavior algorithms has value in advancing modelling with the ability to lower uncertainty and develop robust behavioral frameworks for circulation and evacuation. In these cases, speed parameters may not be as critical, but rather the actual decision-making process being the most critical to how the building and space will be used. Modern advancements and critical thinking are now emerging to how individuals interact with one another based on more complicated behavioral and decision-making theories, particularly as the field becomes very multi-disciplinary (bringing together psychologists, sociologists, engineers etc.). There is difficulty in forecasting when these new models that offer new ways to describe evacuation and circulation will appear for commercial and research use. However, based on the discussion made in Chap. 4 it is still of value that both emergency and non-emergency datasets are available to verify and validate. These datasets may take on various means. With regard to AI and Computer Vision, there are several datasets that have been compiled of pedestrians, such as STCrowd which includes both LiDAR and video footage (Cong et al. 2022). However, the data is designed for development and benchmarking purposes, and therefore provides less value for use as an emergency dataset. The authors have recognized that recordings from many past studies are now missing or unavailable, which makes for the analysis of the quality of the movement data hard to interpret. As technology has improved and storage space less of an issue it is important that film (where allowed) be preserved. It would also be of benefit to standardize the quality of the filming process so that various video analysis tools may have adaptability in secondary analysis of said videos.

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5.2.3 Semi-Autonomous Technologies for Human Movement Data Manual tracking methods have historically been used to collect information such as movement speeds and details about people. This usually consists of timing a person as they travel a known distance between two points. In field studies this is conceptually easy. However accurate timing is difficult, particularly problematic in highly crowded areas. Playing back prerecorded video is possible, but again this procedure is time consuming. While it is possible to consider personal details, behaviors, and contexts using prerecorded video, it is not possible to collect precise position data. One method involved the use of cameras mounted high above the area to be analyzed, which gave effective results for pedestrian counting, but movement speed generation was limited to manually timing the movement of each track between two lines (Li et al. 2012). Manual tracking is best suited for linear pathways and simple trajectories. When there are multiple entrances and exits, the distances travelled must be determined for each pathway, which increases the processing time. In previous studies carried out, manual tracking could require several minutes per person tracked (Chin et al. 2022a, b). Efforts by the authors have been underway to develop technologies to ease the time required for extracting movement data from video recordings. While these efforts were previously shown (Gales et al. 2022; Young and Gales 2022) by the authors, they are further described and explained in terms of their limitations and where more automotive approaches are being developed for movement data development herein. The methodology allows for tracking to be done automatically using computer vision software, while incorporating manual tagging elements and has therefore been named Semiautomated Tracking. The provision of this tracking framework (Young and Gales 2022; Young 2021) is essentially open access in the authorships resources and allows for the replication by other users. Care by the user should be made to ensure that the user adequately understands the limitations and accuracy of the analysis technique before using over traditional measurement techniques. Semiautomated Tracking as developed by the authors is a modification of an opensource kinematics analysis software called Kinovea (Charmant, Kinovea, n.d.). The software by default can analyze the movement of selected points and make corrections for camera perspective angles and scaling. This allows for the output of distances and instantaneous speeds. Kinovea performs object tracking frame-by-frame and is therefore useful in scenarios where people may not have a defined start and end point. The requirement for users to manually specify the point to track permits manual tagging for more specific analysis. Kinovea and other video tracking technologies have advantages to the level of detail that can be applied to an analysis. While LiDAR and infrared can be used in more restrictive environments where ethics or institutional authorities prohibit filming, they may not capture finer details which may be useful for identifying demographics or other features such as disabilities, mobility devices, or luggage which may have an impact on a pedestrian’s movement speeds, personal space, and/or decisions made. Kinovea’s manual tagging and tracking means that these finer details can be recorded and analyzed.

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One important consideration is that Kinovea does not directly output statistical distributions of walking speeds for a set of data generated from multiple tracks, necessitating additional manual calculations and analysis. Each tracked pedestrian or object would need to be sorted or categorized if using a more detailed analysis method. To provide a faster method of analysis, modifications through additional software were developed to process the output data from Kinovea. To increase the speed of processing pedestrian motion videos in Kinovea, the authors developed two additional pieces of software (see Young 2021; Young and Gales 2022). The first was an automation script which was designed to help automate the input processes, whereas the second was a post-processing software used to generate the statistical distributions taken from Kinovea’s outputs. To expedite the creation of tags on tracked people and objects, a custom script is used to automate keypresses and jump to relevant data entry fields. This saves time by mimicking the inputs that a user would need to make. When this script is enabled, users are automatically prompted to enter a string of characters in the ‘Name’ field to represent the tags desired. The window automatically opens when a person is selected to end tracking, and the prompt window automatically closes and stops tracking once the tags are entered. The tagging window shows the person being tagged, but the user can also determine this while tracking is ongoing. The tags can be used to represent practically any attribute as specified by the user (Fig. 5.3). Kinovea can output the raw x–y coordinates of each tracked object for each frame as a text file. Custom scripts have been coded to interpret these files and calculate movement speeds categorized by demographics tag. The process is largely automated, with the user only needing to select the output file generated by Kinovea.

Fig. 5.3 Tagging window in Kinovea (Tags l and s applied, indicating late passenger with suitcase)

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These scripts use the coordinate data and time between frames to calculate instantaneous and average walking speeds for each tracked person. Excel VBA has been employed to accomplish this in an iterative fashion which scales with the number of frames and number of people tracked. The data is automatically separated and compiled according to each tag assigned in the tagging process. The script can also make adjustments for different camera frame rates, as well as detect and accommodate duplicated frames in security camera footage. The final output of the script is the average walking speeds and counts for each demographic analyzed, as well as an overall analysis of the entire population processed. Prior to the enhancements of Semiautomated tracking, Larsson and Friholm of Lund University used Kinovea to track pedestrian motion, including walking speed and body movement in a lab environment (Larsson and Friholm 2019). Their results were compared to an automated motion tracking system which required the participants to wear tracking markers, to determine the effectiveness of each system for a detailed analysis of limb motion, walking speeds, and separation between pedestrians. While the automated optical motion capture method demonstrated higher data accuracy, lower user dependency, and faster analysis, there were drawbacks identified which made the method unfeasible for crowd investigations. Equipment costs were significant and applying markers to a general crowd was not possible. Video tracking took longer to process but was less expensive and could be used in a realistic crowd investigation (Larsson and Friholm 2019). Table 5.1 has been adapted further to incorporate LiDAR, Manual Tracking, and updated capabilities of video analysis using Semi-automated Tracking. Kinovea, as with all line-of-sight methodologies suffer from challenges in occlusion when a person passes behind another person or object. Once line-of-sight is lost, it becomes very difficult or impossible for tracking to continue. Infrared and LiDAR cameras are particularly susceptible as even transparent obstructions such as glass walls or barriers will block the cameras. When examining large crowds, the same occlusion issues exist as infrared signatures start to blend and shadows are cast by the first object in the LiDAR’s line of sight. Steep or near vertical camera angles may make it easier for individuals in denser crowds to be tracked, as shown by PeTrack’s methodology (Boltes and Seyfried 2013). One benefit of Semiautomated Tracking is that partial trajectories can be used, so if a person or object is partially occluded, tracking can be done during the non-occluded parts of the footage. Higher resolutions and frame rates also result in more detail which may improve tracking accuracy and abilities for denser crowds. In Kinovea, cameras are best set up with four measurable points visible in a rectangular shape, such as a floor tile pattern. This allows for calibration to correct viewing angles and distortion. The camera lens distortion must also be recorded and taken into effect using parameters set within Kinovea or through corrections applied to the raw video in video editing software.

5.2.4 Autonomous Technologies for Human Movement Data As the field of data collection continues to progress, it would be unfeasible to analyze the vast amounts of data and information generated using traditional manual methods,

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Table 5.1 Data capture methods (modified from Larrsson and Friholm 2019) Evaluation aspect Video analysis (semiautomated tracking)

Optical motion capture

LiDAR

Manual tracking

User-friendliness

High

High

Moderate

High

Economical aspects

Cheap even if Expensive if hardware needs to hardware needed be purchased

Very expensive if hardware needed

Cheap, but costs increase with duration and size

Data accuracy

Moderate, depends on sampling rate

High, sampling rate is 100 measurements per second

High, sampling rate is up to 50 measurements per second

Low, only measures average speed

User dependency High, many elements have user dependent aspects

Low, only preparation has user dependent aspects

Low, only preparation has user dependent aspects

High, everything has user dependent aspects

Can handle obstructed markers

Yes, but only if obstructions are for short periods of time

Yes, especially if multiple sensors used

Yes, as only start and endpoint are tracked

Can analyze Yes, but accuracy without the use of will be lower markers

No

Yes, markers not required

Only unmarked tracking possible

Time consuming: Depends on collection number of participants and tests

Depends on number of participants and tests

Real-time data collection

High, depends on number of participants and tests

Time consuming: Moderate if analysis analysis is automatic, high if tracking environment congested

None, analysis is None, analysis completed during is completed the experiment during the experiment

High, automatic analysis not possible

Partial trajectories possible

Yes

Yes

Yes

No

Can be used for crowd investigation in public spaces

Yes, especially if cameras already exist

N/A, cannot place markers for tracking on general public

Yes, but requires mounting of custom LiDAR equipment

No, method does not scale well to very large crowds

Markers can be manually estimated or data cut short

and even semiautomated tracking may be overwhelmed. However, the large datasets may also lend themselves well to automated behaviour and movement analysis using AI and Machine Learning. By employing the right techniques and technologies, a framework can be developed to automatically detect, classify, and model the movement of the analyzed populations occupying the structure.

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For analysis, Computer Vision (CV) is an essential element of the framework. CV software allows computers to automatically detect patterns and objects from images or video frames, much like a human would. This generates movement and/or classification data that can then be passed along for further analysis. Kinovea would be one such example of Computer Vision for its tracking capabilities. Artificial Neural Networks (ANN) are a form of Machine Learning that computes the various weights and biases of different inputs to correctly produce an output using training data. The training process is iterated, allowing the neural network to determine patterns that can accurately classify data. This can then be employed in similar new situations to generate similar classification results and perform appropriate actions. Convolutional Neural Networks (CNN) are a subset of ANNs, applying several layers of analysis to the training data to find patterns that can differentiate objects at differing levels of detail, until the deepest, final layer can accurately identify and classify objects that it sees in the image. In the context of crowd analysis, CNNs can automatically identify and track pedestrians but also identify key features such as baggage if desired (Ramadan et al. 2022). Considering they differentiate the input these neural networks are known as discriminative models. Conversely, generative models use probability distributions to produce a result within the range of trained data. Generative Adversarial Networks, (GAN) are a form of generative ANN that uses a discriminative CNN as part of the training process. The training of a GAN involves producing a synthesized output that the discriminative model cannot differentiate from real training data. While GANs are more well-known for image editing and AI Art, the principles can be applied to estimate potential pedestrian trajectories (Gupta et al. 2018). This can output a variety of potential paths that a pedestrian could take, but notably does not seek to replicate a single situation. Instead, the process can be repeated to generate a range of realistic potential scenarios that a pedestrian could then execute. A common theme amongst the above elements is the open-source nature of the code and algorithms at the base level. Open-source software provides the source code (required to build the software) and a license for others to view and modify for their own purposes. This allows for transparency regarding the algorithms used, rapid innovation and interdisciplinary collaboration by people across the globe, and quick adaptation to suit new projects (Krogh and Spaeth 2007). As new discoveries are made, other groups can quickly access the latest code to integrate into their models. Kinovea is open source, hence its use as the basis for semiautomated tracking. One of the more popular code frameworks for computer vision is OpenCV, which has been developed as an open-source code library of computer vision tools since 2000 (OpenCV 2023). Considering the real-time processing capabilities of these systems, much of this technology was developed from an autonomous vehicles perspective. For example, regular monocular cameras coupled with Computer Vision technologies and Convolutional Neural Networks were employed to autonomously monitor crowd densities and movement. This method can be compatible with preexisting security camera footage and therefore does not require the expense and labour of installing special equipment (Junayed and Islam 2022; Amirgholipour et al. 2018). To maximize coverage and handle tracking across multiple devices, omnidirectional overhead

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cameras can be employed. These cameras provide a 360-degree field of view, capturing everything around them. As the fields of view from multiple cameras overlap, it is possible to determine when a person is being tracked by both cameras and continue to track them as they transition from one camera to the next (Montero et al. 2023). The technique of having multiple cameras that view the same people from different angles is similar to the use of Stereo Cameras. These units consist of two camera lenses mounted a specific distance apart, where differences between the slightly offset images are used to determine depth and therefore detect movement in 3D space. From a top-down view, this can be used to determine individual people and track their movement (Boltes and Seyfried 2013). Stereo cameras are now commercialized and available for purchase, displaying both imagery and depth data (RGB-D) for computer vision systems to process. This technology is particularly useful for counting pedestrians and determining the location of specific elements of interest (e.g. head tracking) (Lian 2019). It can also be used to determine relative speeds, directions, and flows which can detect changes in overall behaviour (Horii 2020). While camera techniques provide additional visual context to the behaviors (and therefore can be coupled with further visual analysis), the recording and storage of pedestrian motion data can be problematic, especially in organizations, states, or countries where recording is more restricted. Different methods that do not use imagery have been developed to generate fully anonymous data that does not capture personally identifiable information. In Australia, a project analyzing crowd behaviour at railway stations made use of Kinect sensors (Virgona et al. 2015). The use of LiDAR sensors to generate point clouds is more common, as these systems can be paired with AI detection software to identify pedestrians or other moving objects (see Fig. 5.4). The software may also persistently track the trajectories of people as they move throughout the coverage area. Handoff of people between different sensor coverage areas can also be handled within the software (Jeong et al. 2019).

Fig. 5.4 LiDAR system in operation, tracking across multiple sensors (author’s equipment and image)

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These technologies of these systems do not stand alone; researchers have had success combining multiple sensor types together to provide context to point cloud data, improve AI tracking capabilities, or otherwise provide enhancements. LiDAR and Camera fusion is a common form, as this allows software to reliably identify the location of an object via LiDAR point clouds, and then fine tune and classify the information using the camera imagery (Dimitrievski et al. 2019). While this is currently limited to identifying a person vs a vehicle, it could also be used for more in-depth analysis by incorporating other previously discussed technologies, such as differentiating between different encumbrances such as baggage (Ramadan et al. 2022) Additional applications could eventually include the recognition of persons with disabilities and their subsequent needs to safely traverse the space. To control and monitor crowds, AI-enabled crowd analysis, databasing, and datadriven pedestrian modelling may soon be capable of integration with digital twin systems. A digital twin is an interactive virtual copy of an object or system that is synchronized with its current status. In the case of buildings and infrastructure, the digital twin can then receive interactive inputs from sensors and operators to run simulations, help advise on decision-making, and automate building operations. Recently, researchers have demonstrated the benefits of digital twins for smart cities, modeling the circulation of pedestrians on city streets for planning purposes (White et al. 2021). Additional papers have proposed the use of digital twins for aviation terminal (Diange and Haiyun 2022) and maritime vessel (Arrichiello and Gualeni 2020) evacuation training and management. By combining Digital Twins with pedestrian analysis and modelling, the building could monitor occupancy levels, compute movement speeds for the occupants, and model potential evacuation or circulation scenarios. If a scenario where crowd control is needed, the digital twin could suggest or implement measures to safely direct crowds to where they need to go (Liu et al. 2020). This could be particularly useful for evacuation or circulation scenarios where some areas are rendered impassible and alternative movement strategies are needed. The real-time nature of automated analysis and data-driven modelling allows the model to constantly monitor the egress and propose changes and alternatives to improve egress. Ultimately, the developing capabilities of AI analysis and data-driven modelling could allow for safer, more flexible egress of future stadia that accounts for the needs of the people that the building serves. Through the use of data collection and analysis, managers and engineers can better understand the needs of their guests in contemporary stadia: accessibility concerns can be identified and addressed to improve inclusivity and in preparation to serve an aging population; flow rates, movement speeds, and other behaviour from people in the stadium can be compared with previous situations to model potential scenarios for circulation and evacuation; and following the development of the technology, digital twins could potentially perform these tasks automatically, keeping the people safe to enjoy the games in the stadia of the future.

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