Crowd behaviour during high-stress evacuations in an immersive virtual environment

With or Without You: Effect of Contextual and Responsive Crowds on VR-based Crowd Motion Capture

While data is vital to better understand and model interactions within human crowds, capturing real crowd motions is extremely challenging. Virtual Reality (VR) demonstrated its potential to help, by immersing users into either simulated virtual crowds based on autonomous agents, or within motion-capture-based crowds. In the latter case, users' own captured motion can be used to progressively extend the size of the crowd, a paradigm called Record-and-Replay (2R). However, both approaches demonstrated several limitations which impact the quality of the acquired crowd data. In this paper, we propose the new concept of contextual crowds to leverage both crowd simulation and the 2R paradigm towards more consistent crowd data. We evaluate two different strategies to implement it, namely a Replace-Record-Replay (3R) paradigm where users are initially immersed into a simulated crowd whose agents are successively replaced by the user's captured-data, and a Replace-Record-Replay-Responsive (4R) paradigm where the pre-recorded agents are additionally endowed with responsive capabilities. These two paradigms are evaluated through two real-world-based scenarios replicated in VR. Our results suggest that the behaviors observed in VR users with surrounding agents from the beginning of the recording process are made much more natural, enabling 3R or 4R paradigms to improve the consistency of captured crowd datasets.

Experiments as Code and its application to VR studies in human-building interaction

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

A quantitative comparison of virtual and physical experimental paradigms for the investigation of pedestrian responses in hostile emergencies

Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

Emergence and collapse of reciprocity in semiautomatic driving coordination experiments with humans

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

Analysis of the use of behavioral data from virtual reality for calibration of agent-based evacuation models

Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior. However, empirical studies which would assess validity of VR-based data for such purposes are still rare and considerably lacking in the agent-based evacuation modeling domain. This study explores opportunities that the VR behavioral data may bring for refining outputs of agent evacuation models. To this end, this study employed multiple input settings of agent-based evacuation models (ABEMs), including those based on the data gathered from the VR evacuation experiment that mapped out evacuation behaviors of individuals within the building. Calibration and evaluation of models was based on empirical data gathered from an original evacuation exercise conducted in a real building (N = 35) and its virtual twin (N = 38). This study found that the resulting predictions of single agent models using data collected in the VR environment after proposed corrections have the potential to better predict real-world evacuation behavior while offering desirable variance in the data outputs necessary for practical applications.

In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty

Group decision making under uncertainty often requires groups to balance exploration of their environment with exploitation of the seemingly best option. In order to succeed at this collective induction, groups need to merge the knowledge of all group members and combine goal-oriented and social motivations (i.e., group cohesion). This paper presents three studies that investigate whether more cohesive groups perform worse at collective induction tasks as they spend less time exploring possible options. Study 1 simulates group decision making with the ε-greedy algorithm in order to identify suitable manipulations of group cohesion and investigate how differing exploration lengths can affect outcomes of group decisions. Study 2 (N = 108, 18 groups á 6 participants) used an experimental manipulation of group cohesion in a simple card choice task to investigate how group cohesion might affect group decision making when only limited social information is available. Study 3 (N = 96, 16 groups á 6 participants) experimentally manipulated group cohesion and used the HoneyComb paradigm, a movement-based group experiment platform, to investigate which group processes would emerge during decision making and how these processes would affect the relationships between group cohesion, exploration length, and group decision making. Study 1 found that multiplicative cohesion rewards have detrimental effects on group decision making, while additive group rewards could ameliorate negative effects of the cohesion reward, especially when reported separately from task rewards. Additionally, exploration length was found to profoundly affect decision quality. Studies 2 and 3 showed that groups could identify the best reward option successfully, regardless of group cohesion manipulation. This effect is interpreted as a ceiling effect as the decision task was likely too easy to solve. Study 3 identified that spatial group cohesion on the playing field correlated with self-reported entitativity and leader-/followership emerged spontaneously in most groups and correlated with self-reported perceptions of leader-/followership in the game. We discuss advantages of simulation studies, possible adaptations to the ε-greedy algorithm, and methodological aspects of measuring behavioral group cohesion and leadership to inform empirical studies investigating group decision making under uncertainty.

Mobile eye tracking applied as a tool for customer experience research in a crowded train station

Train stations have increasingly become crowded, necessitating stringent requirements in the design of stations and commuter navigation through these stations. In this study, we explored the use of mobile eye tracking in combination with observation and a survey to gain knowledge on customer experience in a crowded train station. We investigated the utilization of mobile eye tracking in ascertaining customers' perception of the train station environment and analyzed the effect of a signalization prototype (visual pedestrian flow cues), which was intended for regulating pedestrian flow in a crowded underground passage. Gaze behavior, estimated crowd density, and comfort levels (an individual's comfort level in a certain situation), were measured before and after the implementation of the prototype. The results revealed that the prototype was visible in conditions of low crowd density. However, in conditions of high crowd density, the prototype was less visible, and the path choice was influenced by other commuters. Hence, herd behavior appeared to have a stronger effect than the implemented signalization prototype in conditions of high crowd density. Thus, mobile eye tracking in combination with observation and the survey successfully aided in understanding customers' perception of the train station environment on a qualitative level and supported the evaluation of the signalization prototype the crowded underground passage. However, the analysis process was laborious, which could be an obstacle for its practical use in gaining customer insights.

Effect of electrical stimulation amplitude on dynamic behavior of mice during evacuation

Profound examination of the dynamic behavior of pedestrians during evacuation can significantly reduce the number of associated accidents. Conducting experiments on animals can help obtain deep insight into the dynamic behavior of pedestrians. Previous experiments using insects, such as ants and woodlice, showed large differences between the dynamic behaviors of insects and humans. However, systematic studies on the behavioral characteristics (e.g., velocity) of mice under electrical stimulation conditions have not been reported. Therefore, this study was conducted to investigate changes in the dynamic behavior of mice during evacuation caused by electric shock. Electrical stimulation was supplied through their feet during evacuation. The average velocity, desired velocity (maximum instantaneous velocity), average velocity in the congestion zone, and escape time were measured and analyzed. According to the results, the desired velocity and escape time increased in proportion to the amplitude of the electrical stimulation; however, the average velocity decreased. Consequently, the level of emergency of mice is affected by both the amplitude of electrical stimulation and the number density in congestion area as in human experiments.

MoTiS Parameters for Expressive Multi-Robot Systems: Relative Motion, Timing, and Spacing

Multi-robot systems are moving into human spaces, such as working with people in factories (Bacula et al., in: Companion of the 2020 ACM/IEEE international conference on human–robot interaction, pp 119–121, 2020) or in emergency support (Wagner in Front Robot AI 8, 2021; Baxter et al., in: Autonomous robots and agents, Springer, pp 9–16, 2007) and it is crucial to consider how robots can communicate with the humans in the space. Our work evaluates a parameter framework to allow multi-robot groups of x, y, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta $$\end{document}θ robots to effectively communicate using expressive motion. While expressive motion has been extensively studied in single robots (Knight et al., in: 2016 IEEE international conference on intelligent robots and systems (IROS), IEEE, 2016; Bacula and LaViers in Int J Soc Robot, 1–16, 2020; Dragan et al., in: 2013 8th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 301–308, 2013; Kirby et al., in: The 18th IEEE international symposium on robot and human interactive communication, 2009, RO-MAN 2009, IEEE, pp 607–612, 2009), moving to multi-robots creates new challenges as the state space expands and becomes more complex. We evaluate a hierarchical framework of six parameters to generate multi-robot expressive motion consisting of: (1) relative direction, (2) coherence, (3) relative speed, (4) relative start time, (5) proximity, and (6) geometry. We conducted six independent online studies to explore each parameter, finding that four out of six of the parameters had significant impact on people’s perception of the multi-robot group. Additional takeaways of our studies clarify what humans interpret as a robot group, when the group is perceived positively versus negatively, and the critical role of architectural floor plan in interpreting robot intent.

Pedestrians safety perception and crossing behaviors in narrow urban streets: An experimental study using immersive virtual reality technology

Virtual reality (VR) technology emerges as a promising tool for investigating human perception and behavior in highly controlled, immersive, and risk-free environments. This study proposed to apply simulated VR technology to investigate the interactions between perceived crash risk and behavior patterns in a road crossing with changes in the safety-related environmental attributes. In the context of the 8-meter-wide segment in a residential block, 35 VR environments with variations of six environmental attributes were generated. Two hundred participants were recruited for the experiment. The measured behavioral outcomes were 1) waiting and reaction time in the decision phase before crossing and 2) crossing speed and gait variability in the crossing phase. Random effect regression and multi-level structural equation models were constructed to test the study hypotheses. The results demonstrated that environmental attributes, including barriers to visibility (coefficient = 0.446), geometric patterns (coefficient = -0.625), and pavement signs (coefficient = -0.502), were associated with the pedestrians' perceived risk, but the influence varied by street types. In addition, changes in the perceived threats to pedestrians were found to mediate the environment-crossing behavior relationship (coefficient of the indirect effect = 0.679). Those who perceive higher crash risk took longer to decide to start walking at a crosswalk and tended to walk in haste while crossing the road. Using VR technology, the present study addressed an inter-relationship between environmental characteristics, cognition, and crossing behavior, contributing to better knowledge on road safety interventions to reduce the risk of pedestrian-involved crashes.

The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements.

Crowd-Sourced Identification of Characteristics of Collective Human Motion

Crowd simulations are used extensively to study the dynamics of human collectives. Such studies are underpinned by specific movement models, which encode rules and assumptions about how people navigate a space and handle interactions with others. These models often give rise to macroscopic simulated crowd behaviours that are statistically valid, but which lack the noisy microscopic behaviours that are the signature of believable real crowds. In this article, we use an existing Turing test for crowds to identify realistic features of real crowds that are generally omitted from simulation models. Our previous study using this test established that untrained individuals have difficulty in classifying movies of crowds as real or simulated, and that such people often have an idealised view of how crowds move. In this follow-up study (with new participants) we perform a second trial, which now includes a training phase (showing participants movies of real crowds). We find that classification performance significantly improves after training, confirming the existence of features that allow participants to identify real crowds. High-performing individuals are able to identify the features of real crowds that should be incorporated into future simulations if they are to be considered realistic.

Experimental Study on the Evaluation and Influencing Factors on Individual's Emergency Escape Capability in Subway Fire

Studying an individual’s emergency escape capability and its influencing factors is of great practical significance for evacuation and escape in subway emergencies. Taking Zhengzhou Zijing Mountain Subway station as the prototype, and using VR technology, a virtual subway fire escape scene was built. Combined with the total escape time, the total contact time with fire, and the total contact time with smoke, it proposed a calculation formula on emergency escape capability. A total of 34 participants with equal gender distribution were recruited to carry out the virtual subway fire escape experiment, and participants’ physiological data (heart rate variability, skin conductance) were real-time recorded by ErgoLAB V3.0 throughout the whole experiment. The emergency escape capability of each participant was evaluated quantitatively, and the related influencing factors were analyzed. The results show that for the age ranges (19–22 years old) in the experiment, the emergency escape capability of women is significantly lower than that of men (p < 0.05); although there is no significance in emergency escape capability in DISC personality types (p > 0.05), the mean emergency escape capability of people with influence personality type is the worst, and that of people with compliance type is the best; during virtual fire escape vs. baseline, Mean_SC and Mean_HR both increased very significantly (all p < 0.01), and participants were under stress during their virtual fire escape. There is a significant negative correlation between emergency escape capability and LF_increase_rate (p < 0.05), and a remarkably significant negative correlation between emergency escape capability and LF/HF_increase_rate (p < 0.01); the greater the increase rate of LF or LF/HF, the smaller the emergency escape capability, with excessive stress probably not being conducive to emergency escape. There is a very significant negative correlation between an individual’s emergency escape capability and the degree of familiarity with the Zijing Mountain subway station (p < 0.01). The findings provide references and suggestions on the emergency management and emergency evacuation for government and subway departments.

Pedestrian flow in two dimensions: Optimal psychological stress leads to less evacuation time and decongestion

Collective motion is an innate ability of all living systems, which depends on physiological and psychosocial factors in the case of humans. Such a collective organization is becoming of great interest in collective motion in human crowds. Using a cellular automaton (CA) simulation model, we demonstrate that emergency egress from a two-dimensional corridor with optimal stress leads to less evacuation time and efficient mass evacuations. We study how three types of stress (i.e., mild stress, optimal stress, and anxiety) described in the literature have a significant impact on the collective dynamics. We found that low-stress levels could decrease the evacuation time in an entire occupied room since agents choose alternative routes rather than the shortest path to the exit and display cooperative behavior. Therefore, the combination of mild and optimal stress can lead to efficient evacuations. Also CA simulations may be used to find safer and more efficient ways to conduct mass evacuation procedures.

A history of crowd simulation: the past, evolution, and new perspectives

This paper aims to discuss the past, evolution, and new perspectives in crowd simulation. Many work have been produced and published in this area that was launched approximately 30 years ago. In this paper, we re-visited the main aspects of the area, presenting the periods and evolution we had in the past. In addition, we also discuss the present and possible trends for the future.

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence.

Exit choice during evacuation is influenced by both the size and proportion of the egressing crowd

It is unclear how building occupants take information from the social and built environment into account when choosing an egress route during emergency evacuation. Conflicting tendencies have been previously reported: to follow the crowd, to avoid congestion, and to avoid unknown egress routes alone. We hypothesize that these tendencies depend on an interaction between social influence and the affordances (opportunities for egress) of the built environment. In three virtual reality (VR) experiments (each N = 15), we investigated how social influence interacts with the affordances of available exits to determine exit choice. Participants were immersed in a crowd of virtual humans walking to the left or right exit, and were asked to walk to one of the exits. Experiment 1 tested the role of social influence by manipulating both the proportion of the crowd walking toward one exit (Crowd Proportion of 0 to 100%, in 10% increments) and the absolute number of virtual humans going to the exit (Crowd Size of 10 or 20). Experiment 2 tested the role of affordances by introducing two visible exit doors (1m width) in a closed room, and following the same protocol. Experiment 3 tested larger exit doors (3m width) that afford rapid egress for more people. In the small crowd, participants were increasingly likely to follow the majority as its proportion increased. In the large crowd, however, participants tended to avoid the more crowded exit if the doors were narrow (Experiment 2), but not if the doors were wide (Experiment 3). Participants tended to follow a 100% majority in all experiments, thereby avoiding going to an exit alone. We propose that the dynamics of exit choice can be understood in terms of competition between alternative egress routes: the attraction of an exit increases with the proportion of the crowd moving toward it, becoming dominant at 100%, but decreases with the absolute number in the crowd moving toward it, relative to the exit's affordance for egress.

Tracking multiple perspectives: Spontaneous computation of what individuals in high entitative groups see

Perspective-taking ability is crucial for supporting social interactions. It has been widely suggested that the calculation of an individual's perspective is spontaneous. Nevertheless, people typically engage with more than one individual, and computing what individuals in a crowd see is important. The current study explored whether people spontaneously compute the perspectives of individuals displayed in a crowd. The classic visual perspective-taking task was adopted, but the picture of the room was presented with four human avatars facing two walls. The results showed that if the crowd of individuals was treated as a high entitative group, when none of the perspectives of the individuals contained the same number of discs as that from the perspective of the participant, the judgment of the participant's perspective was slower than when a proportion of the perspectives of the individuals displayed in the crowd were consistent with the participant's perspective, even if the perspectives of the multiple individuals in a crowd were not explicitly noticed. This altercentric intrusion effect was not present when the crowd had low entitativity. These findings were replicated by using different methods to operationalize group entitativity. Hence, this study demonstrates that spontaneously tracking the perspectives of individuals displayed in a crowd has a boundary condition and that people can spontaneously compute what individuals in high entitative groups see.

Exploring the Spatial Relationships Between Real and Virtual Experiences: What Transfers and What Doesn't

Virtual environments are commonly used to assess spatial cognition in humans. For the past few decades, researchers have used virtual environments to investigate how people navigate, learn, and remember their surrounding environment. In combination with tools such as electroencephalogram, neuroimaging, and electrophysiology, these virtual environments have proven invaluable in their ability to help elucidate the underlying neural mechanisms of spatial learning and memory in humans. However, a critical assumption that is made whenever using virtual experiences is that the spatial abilities used in the navigation of these virtual environments accurately represents the spatial abilities used in the real-world. The aim of the current study is to investigate the spatial relationships between real and virtual environments to better understand how well the virtual experiences parallel the same experiences in the real-world. Here, we performed three independent experiments to examine whether spatial information about object location, environment layout, and navigation strategy transfers between parallel real-world and virtual-world experiences. We show that while general spatial information does transfer between real and virtual environments, there are several limitations of the virtual experience. Compared to the real-world, the use of information in the virtual-world is less flexible, especially when testing spatial memory from a novel location, and the way in which we navigate these experiences are different as the perceptual and proprioceptive feedback gained from the real-world experience can influence navigation strategy.

A Turing test for crowds

The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called 'Turing test' for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is 'Can human observers distinguish between real and simulated crowds?' In two studies with student volunteers (n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

Gait coordination in overground walking with a virtual reality avatar

Little information is currently available on interpersonal gait synchronization in overground walking. This is caused by difficulties in continuous gait monitoring over many steps while ensuring repeatability of experimental conditions. These challenges could be overcome by using immersive virtual reality (VR), assuming it offers ecological validity. To this end, this study provides some of the first evidence of gait coordination patterns for overground walking dyads in VR. Six subjects covered the total distance of 27 km while walking with a pacer. The pacer was either a real human subject or their anatomically and biomechanically representative VR avatar driven by an artificial intelligence algorithm. Side-by-side and front-to-back arrangements were tested without and with the instruction to synchronize steps. Little evidence of spontaneous gait coordination was found in both visual conditions, but persistent gait coordination patterns were found in the case of intentional synchronization. Front-to-back rather than side-by-side arrangement consistently yielded in the latter case higher mean synchronization strength index. Although the mean magnitude of synchronization strength index was overall comparable in both visual conditions when walking under the instruction to synchronize steps, quantitative and qualitative differences were found which might be associated with common limitations of VR solutions.

Wise or mad crowds? The cognitive mechanisms underlying information cascades

Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others' choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift-diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accurately capturing how individuals integrate personal and social information dynamically over time and when their decisions were timed. Our results show the importance of the interrelationships between accuracy, confidence, and response time in shaping the quality of information cascades. The model reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.

Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality

Dense crowds in public spaces have often caused serious security issues at large events. In this paper, we study the 2010 Love Parade disaster, for which a large amount of data (e.g. research papers, professional reports and video footage) exist. We reproduce the Love Parade disaster in a three-dimensional computer simulation calibrated with data from the actual event and using the social force model for pedestrian behaviour. Moreover, we simulate several crowd management strategies and investigate their ability to prevent the disaster. We evaluate these strategies in virtual reality (VR) by measuring the response and arousal of participants while experiencing the simulated event from a festival attendee's perspective. Overall, we find that opening an additional exit and removing the police cordons could have significantly reduced the number of casualties. We also find that this strategy affects the physiological responses of the participants in VR.

The interaction between map complexity and crowd movement on navigation decisions in virtual reality

A carefully designed map can reduce pedestrians' cognitive load during wayfinding and may be an especially useful navigation aid in crowded public environments. In the present paper, we report three studies that investigated the effects of map complexity and crowd movement on wayfinding time, accuracy and hesitation using both online and laboratory-based networked virtual reality (VR) platforms. In the online study, we found that simple map designs led to shorter decision times and higher accuracy compared to complex map designs. In the networked VR set-up, we found that co-present participants made very few errors. In the final VR study, we replayed the traces of participants' avatars from the second study so that they indicated a different direction than the maps. In this scenario, we found an interaction between map design and crowd movement in terms of decision time and the distributions of locations at which participants hesitated. Together, these findings can help the designers of maps for public spaces account for the movements of real crowds.

Decoding collective communications using information theory tools

Organisms have evolved sensory mechanisms to extract pertinent information from their environment, enabling them to assess their situation and act accordingly. For social organisms travelling in groups, like the fish in a school or the birds in a flock, sharing information can further improve their situational awareness and reaction times. Data on the benefits and costs of social coordination, however, have largely allowed our understanding of why collective behaviours have evolved to outpace our mechanistic knowledge of how they arise. Recent studies have begun to correct this imbalance through fine-scale analyses of group movement data. One approach that has received renewed attention is the use of information theoretic (IT) tools like mutual information, transfer entropy and causation entropy, which can help identify causal interactions in the type of complex, dynamical patterns often on display when organisms act collectively. Yet, there is a communications gap between studies focused on the ecological constraints and solutions of collective action with those demonstrating the promise of IT tools in this arena. We attempt to bridge this divide through a series of ecologically motivated examples designed to illustrate the benefits and challenges of using IT tools to extract deeper insights into the interaction patterns governing group-level dynamics. We summarize some of the approaches taken thus far to circumvent existing challenges in this area and we conclude with an optimistic, yet cautionary perspective.

Occurrence mechanism and coping paths of accidents of highly aggregated tourist crowds based on system dynamics

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents.

Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour

Built structures, such as animal nests or buildings that humans occupy, serve two overarching purposes: shelter and a space where individuals interact. The former has dominated much of the discussion in the literature. But, as the study of collective behaviour expands, it is time to elucidate the role of the built environment in shaping collective outcomes. Collective behaviour in social animals emerges from interactions, and collective cognition in humans emerges from communication and coordination. These collective actions have vast economic implications in human societies and critical fitness consequences in animal systems. Despite the obvious influence of space on interactions, because spatial proximity is necessary for an interaction to occur, spatial constraints are rarely considered in studies of collective behaviour or collective cognition. An interdisciplinary exchange between behavioural ecologists, evolutionary biologists, cognitive scientists, social scientists, architects and engineers can facilitate a productive exchange of ideas, methods and theory that could lead us to uncover unifying principles and novel research approaches and questions in studies of animal and human collective behaviour. This article, along with those in this theme issue aims to formalize and catalyse this interdisciplinary exchange.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

Inferring User Intent using Bayesian Theory of Mind in Shared Avatar-Agent Virtual Environments

We present a real-time algorithm to infer the intention of a user's avatar in a virtual environment shared with multiple human-like agents. Our algorithm applies the Bayesian Theory of Mind approach to make inferences about the avatar's hidden intentions based on the observed proxemics and gaze-based cues. Our approach accounts for the potential irrationality in human behavior, as well as the dynamic nature of an individual's intentions. The inferred intent is used to guide the response of the virtual agent and generate locomotion and gaze-based behaviors. Our overall approach allows the user to actively interact with tens of virtual agents from a first-person perspective in an immersive setting. We systematically evaluate our inference algorithm in controlled multi-agent simulation environments and highlight its ability to reliably and efficiently infer the hidden intent of a user's avatar even under noisy conditions. We quantitatively demonstrate the performance benefits of our approach in terms of reducing false inferences, as compared to a prior method. The results of our user evaluation show that 68.18% of participants reported feeling more comfortable in sharing the virtual environment with agents simulated with our algorithm as compared to a prior inference method, likely as a direct result of significantly fewer false inferences and more plausible responses from the virtual agents.

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

Drawing power of virtual crowds

In 1969, social psychologist Milgram and his colleagues conducted an experiment on a busy city street where passers-by witnessed a set of actors spontaneously looking up towards a building. The experiment showed that the crowd's propensity to mimic the actor's gaze increased with the number of actors that looked up. This form of behavioural contagion is found in many social organisms and is central to how information travels through large groups. With the advancement of virtual reality and its continued application towards understanding human response to crowd behaviour, it remains to be verified if behavioural contagion occurs in walkable virtual environments, and how it compares with results from real-world experiments. In this study, we adapt Milgram's experiment for virtual environments and use it to reproduce behavioural contagion. Specifically, we construct a replica of an indoor location and combine two established pedestrian motion models to create an interactive crowd of 60 virtual characters that walk through the indoor location. The stimulus group comprised a subset of the characters who look up at a random time as the participants explore the virtual environment. Our results show that the probability of looking up by a participant is dependent on the size of the stimulus group saturating to near certainty when three or more characters look up. The role of stimulus size was also evident when participant actions were compared with survey responses which showed that more participants selected to not look up even though they saw characters redirect their gaze upwards when the size of the stimulus group was small. Participants also spent more time looking up and exhibited frequent head turns with a larger stimulus group. Results from this study provide evidence that behavioural contagion can be triggered in the virtual environment, and can be used to build and test complex hypotheses for understanding human behaviour in a variety of crowd scenarios.

Virtual Sensing and Virtual Reality: How New Technologies Can Boost Research on Crowd Dynamics

The collective behavior of human crowds often exhibits surprisingly regular patterns of movement. These patterns stem from social interactions between pedestrians such as when individuals imitate others, follow their neighbors, avoid collisions with other pedestrians, or push each other. While some of these patterns are beneficial and promote efficient collective motion, others can seriously disrupt the flow, ultimately leading to deadly crowd disasters. Understanding the dynamics of crowd movements can help urban planners manage crowd safety in dense urban areas and develop an understanding of dynamic social systems. However, the study of crowd behavior has been hindered by technical and methodological challenges. Laboratory experiments involving large crowds can be difficult to organize, and quantitative field data collected from surveillance cameras are difficult to evaluate. Nevertheless, crowd research has undergone important developments in the past few years that have led to numerous research opportunities. For example, the development of crowd monitoring based on the virtual signals emitted by pedestrians' smartphones has changed the way researchers collect and analyze live field data. In addition, the use of virtual reality, and multi-user platforms in particular, have paved the way for new types of experiments. In this review, we describe these methodological developments in detail and discuss how these novel technologies can be used to deepen our understanding of crowd behavior.

Presence and User Experience in a Virtual Environment under the Influence of Ethanol: An Explorative Study

Virtual Reality (VR) is used for a variety of applications ranging from entertainment to psychological medicine. VR has been demonstrated to influence higher order cognitive functions and cortical plasticity, with implications on phobia and stroke treatment. An integral part for successful VR is a high sense of presence - a feeling of 'being there' in the virtual scenario. The underlying cognitive and perceptive functions causing presence in VR scenarios are however not completely known. It is evident that the brain function is influenced by drugs, such as ethanol, potentially confounding cortical plasticity, also in VR. As ethanol is ubiquitous and forms part of daily life, understanding the effects of ethanol on presence and user experience, the attitudes and emotions about using VR applications, is important. This exploratory study aims at contributing towards an understanding of how low-dose ethanol intake influences presence, user experience and their relationship in a validated VR context. It was found that low-level ethanol consumption did influence presence and user experience, but on a minimal level. In contrast, correlations between presence and user experience were strongly influenced by low-dose ethanol. Ethanol consumption may consequently alter cognitive and perceptive functions related to the connections between presence and user experience.

Urban Land Allocation Model of Territorial Expansion by Urban Planners and Housing Developers

Agent-based models have recently been proposed as potential tools to support urban planning due to their capacity to simulate complex behaviors. The complexity of the urban development process arises from strong interactions between various components driven by different agents. AMEBA (agent-based model for the evolution of urban areas) is a prototype of an exploratory, spatial, agent-based model that considers the main agents involved in the urban development process (urban planners, developers, and the population). The prototype consists of three submodels (one for each agent) that have been developed independently and present the same structure. However, the first two are based on a land use allocation technique, and the last one, as well as their integration, on an agent-based model approach. This paper describes the conceptualization and performance of the submodels that represent urban planners and developers, who are the agents responsible for officially launching expansion and defining the spatial allocation of urban land. The prototype was tested in the Corredor del Henares (an urban–industrial area in the Region of Madrid, Spain), but is sufficiently flexible to be adapted to other study areas and generate different future urban growth contexts. The results demonstrate that this combination of agents can be used to explore various policy-relevant research questions, including urban system interactions in adverse political and socioeconomic scenarios.

Beyond the faster-is-slower effect

The "faster-is-slower" effect arises when crowded people push each other to escape through an exit during an emergency situation. As individuals push harder, a statistical slowing down in the evacuation time can be achieved. The slowing down is caused by the presence of small groups of pedestrians (say, a small human cluster) that temporarily block the way out when trying to leave the room. The pressure on the pedestrians belonging to this blocking cluster increases for increasing anxiety levels and/or a larger number of individuals trying to leave the room through the same door. Our investigation shows, however, that very high pressures alter the dynamics in the blocking cluster and, thus, change the statistics of the time delays along the escaping process. A reduction in the long lasting delays can be acknowledged, while the overall evacuation performance improves. We present results on this phenomenon taking place beyond the faster-is-slower regime.

Efficacy of virtual reality in pedestrian safety research

Advances in virtual reality technology present new opportunities for human factors research in areas that are dangerous, difficult, or expensive to study in the real world. The authors developed a new pedestrian simulator using the HTC Vive head mounted display and Unity software. Pedestrian head position and orientation were tracked as participants attempted to safely cross a virtual signalized intersection (5.5 m). In 10% of 60 trials, a vehicle violated the traffic signal and in 10.84% of these trials, a collision between the vehicle and the pedestrian was observed. Approximately 11% of the participants experienced simulator sickness and withdrew from the study. Objective measures, including the average walking speed, indicate that participant behavior in VR matches published real world norms. Subjective responses indicate that the virtual environment was realistic and engaging. Overall, the study results confirm the effectiveness of the new virtual reality technology for research on full motion tasks.

Pedestrian collective motion in competitive room evacuation

When a sizable number of people evacuate a room, if the door is not large enough, an accumulation of pedestrians in front of the exit may take place. This is the cause of emerging collective phenomena where the density is believed to be the key variable determining the pedestrian dynamics. Here, we show that when sustained contact among the individuals exists, density is not enough to describe the evacuation, and propose that at least another variable –such as the kinetic stress– is required. We recorded evacuation drills with different degrees of competitiveness where the individuals are allowed to moderately push each other in their way out. We obtain the density, velocity and kinetic stress fields over time, showing that competitiveness strongly affects them and evidencing patterns which have been never observed in previous (low pressure) evacuation experiments. For the highest competitiveness scenario, we detect the development of sudden collective motions. These movements are related to a notable increase of the kinetic stress and a reduction of the velocity towards the door, but do not depend on the density.

Collective movements of pedestrians: How we can learn from simple experiments with non-human (ant) crowds

Introduction

Understanding collective behavior of moving organisms and how interactions between individuals govern their collective motion has triggered a growing number of studies. Similarities have been observed between the scale-free behavioral aspects of various systems (i.e. groups of fish, ants, and mammals). Investigation of such connections between the collective motion of non-human organisms and that of humans however, has been relatively scarce. The problem demands for particular attention in the context of emergency escape motion for which innovative experimentation with panicking ants has been recently employed as a relatively inexpensive and non-invasive approach. However, little empirical evidence has been provided as to the relevance and reliability of this approach as a model of human behaviour.

Methods

This study explores pioneer experiments of emergency escape to tackle this question and to connect two forms of experimental observations that investigate the collective movement at macroscopic level. A large number of experiments with human and panicking ants are conducted representing the escape behavior of these systems in crowded spaces. The experiments share similar architectural structures in which two streams of crowd flow merge with one another. Measures such as discharge flow rates and the probability distribution of passage headways are extracted and compared between the two systems.

Findings

Our findings displayed an unexpected degree of similarity between the collective patterns emerged from both observation types, particularly based on aggregate measures. Experiments with ants and humans commonly indicated how significantly the efficiency of motion and the rate of discharge depend on the architectural design of the movement environment.

Practical applications

Our findings contribute to the accumulation of evidence needed to identify the boarders of applicability of experimentation with crowds of non-human entities as models of human collective motion as well as the level of measurements (i.e. macroscopic or microscopic) and the type of contexts at which reliable inferences can be drawn. This particularly has implications in the context of experimenting evacuation behaviour for which recruiting human subjects may face ethical restrictions. The findings, at minimum, offer promise as to the potential benefit of piloting such experiments with non-human crowds, thereby forming better-informed hypotheses.

Understanding human queuing behaviour at exits: an empirical study

The choice of the exit to egress from a facility plays a fundamental role in pedestrian modelling and simulation. Yet, empirical evidence for backing up simulation is scarce. In this contribution, we present three new groups of experiments that we conducted in different geometries. We varied parameters such as the width of the doors, the initial location and number of pedestrians which in turn affected their perception of the environment. We extracted and analysed relevant indicators such as distance to the exits and density levels. The results put in evidence the fact that pedestrians use time-dependent information to optimize their exit choice, and that, in congested states, a load balancing over the exits occurs. We propose a minimal modelling approach that covers those situations, especially the cases where the geometry does not show a symmetrical configuration. Most of the models try to achieve the load balancing by simulating the system and solving optimization problems. We show statistically and by simulation that a linear model based on the distance to the exits and the density levels around the exit can be an efficient dynamical alternative.

How Simple Hypothetical-Choice Experiments Can Be Utilized to Learn Humans' Navigational Escape Decisions in Emergencies

How humans resolve non-trivial tradeoffs in their navigational choices between the social interactions (e.g., the presence and movements of others) and the physical factors (e.g., spatial distances, route visibility) when escaping from threats in crowded confined spaces? The answer to this question has major implications for the planning of evacuations and the safety of mass gatherings as well as the design of built environments. Due to the challenges of collecting behavioral data from naturally-occurring evacuation settings, laboratory-based virtual-evacuation experiments have been practiced in a number of studies. This class of experiments faces the traditional question of contextual bias and generalizability: How reliably can we infer humans' behavior from decisions made in hypothetical settings? Here, we address these questions by making a novel link between two different forms of empirical observations. We conduct hypothetical emergency exit-choice experiments framed as simple pictures, and then mimic those hypothetical scenarios in more realistic fashions through staging mock evacuation trials with actual crowds. Econometric choice models are estimated based on the observations made in both experimental contexts. The models are contrasted with each other from a number of perspectives including their predictions as well as the sign, magnitude, statistical significance, person-to-person variations (reflecting individuals' perception/preference differences) and the scale (reflecting context-dependent decision randomness) of their inferred parameters. Results reveal a surprising degree of resemblance between the models derived from the two contexts. Most strikingly, they produce fairly similar prediction probabilities whose differences average less than 10%. There is also unexpected consensus between the inferences derived from both experimental sources on many aspects of people's behavior notably in terms of the perception of social interactions. Results show that we could have elicited peoples' escape strategies with fair precision without observing them in action (i.e., simply by using only hypothetical-choice data as an inexpensive, practical and non-invasive experimental technique in this context). As a broader application, this offers promising evidence as to the potential applicability of the hypothetical-decision experiments to other decision contexts (at least for non-financial decisions) when field or real-world data is prohibitively unavailable. As a practical application, the behavioral insights inferred from our observations (reflected in the estimated parameters) can improve how accurately we predict the movement patterns of human crowds in emergency scenarios arisen in complex spaces. Fully-generic-in-parameters, our proposed models can even be directly introduced to a broad range of crowd simulation software to replicate navigation decision making of evacuees.