Pedestrian Leadership and Egress Assistance Simulation Environment (PLEASE)

Over the past decade, researchers have been developing new ways to model pedestrian egress especially in emergency situations. The traditional methods of modeling pedestrian egress, including ow-based modeling and cellular automata, have been shown to be poor models of human behavior at an individual level, as well as failing to capture many important group social behaviors of pedestrians. This has led to the exploration of agent-based modeling for crowd simulations including those involving pedestrian egress. Using this model, we evaluate different heuristic functions for predicting good egress routes for a variety of real building layouts. We also introduce reinforcement learning as a means to represent individualized pedestrian route knowledge. Finally, we implement a group formation technique, which allows pedestrians in a group to share route knowledge and reach a consensus in route selection. Using the group formation technique, we consider the effects such knowledge sharing and consensus mechanisms have on pedestrian egress times

System Issues in Multi-agent Simulation of Large Crowds

Crowd simulation is a complex and challenging domain. Crowds demonstrate many complex behaviours and are consequently difficult to model for realistic simulation systems. Analyzing crowd dynamics has been an active area of research and efforts have been made to develop models to explain crowd behaviour. In this paper we describe an agent based simulation of crowds, based on a continuous field force model. Our simulation can handle movement of crowds over complex terrains and we have been able to simulate scenarios like clogging of exits during emergency evacuation situations. The focus of this paper, however, is on the scalability issues for such a multi-agent based crowd simulation system. We believe that scalability is an important criterion for rescue simulation systems. To realistically model a disaster scenario for a large city, the system should ideally scale up to accommodate hundreds of thousands of agents. We discuss the attempts made so far to meet this challenge, and try to identify the architectural and system constraints that limit scalability. Thereafter we propose a novel technique which could be used to richly simulate huge crowds

Placing large group relations into pedestrian dynamics: psychological crowds in counterflow

Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios

Role of opinion sharing on the emergency evacuation dynamics

Emergency evacuation is a critical research topic and any improvement to the existing evacuation models will help in improving the safety of the evacuees. Currently, there are evacuation models that have either an accurate movement model or a sophisticated decision model. Individuals in a crowd tend to share and propagate their opinion. This opinion sharing part is either implicitly modeled or entirely overlooked in most of the existing models. Thus, one of the overarching goal of this research is to the study the effect of opinion evolution through an evacuating crowd. First, the opinion evolution in a crowd was modeled mathematically. Next, the results from the analytical model were validated with a simulation model having a simple motion model. To improve the fidelity of the evacuation model, a more realistic movement and decision model were incorporated and the effect of opinion sharing on the evacuation dynamics was studied extensively. Further, individuals with strong inclination towards particular route were introduced and their effect on overall efficiency was studied. Current evacuation guidance algorithms focuses on efficient crowd evacuation. The method of guidance delivery is generally overlooked. This important gap in guidance delivery is addressed next. Additionally, a virtual reality based immersive experiment is designed to study factors affecting individuals\u27 decision making during emergency evacuation

Agent-based models of social behaviour and communication in evacuations:A systematic review

Most modern agent-based evacuation models involve interactions between evacuees. However, the assumed reasons for interactions and portrayal of them may be overly simple. Research from social psychology suggests that people interact and communicate with one another when evacuating and evacuee response is impacted by the way information is communicated. Thus, we conducted a systematic review of agent-based evacuation models to identify 1) how social interactions and communication approaches between agents are simulated, and 2) what key variables related to evacuation are addressed in these models. We searched Web of Science and ScienceDirect to identify articles that simulated information exchange between agents during evacuations, and social behaviour during evacuations. From the final 70 included articles, we categorised eight types of social interaction that increased in social complexity from collision avoidance to social influence based on strength of social connections with other agents. In the 17 models which simulated communication, we categorised four ways that agents communicate information: spatially through information trails or radii around agents, via social networks and via external communication. Finally, the variables either manipulated or measured in the models were categorised into the following groups: environmental condition, personal attributes of the agents, procedure, and source of information. We discuss promising directions for agent-based evacuation models to capture the effects of communication and group dynamics on evacuee behaviour. Moreover, we demonstrate how communication and group dynamics may impact the variables commonly used in agent-based evacuation models

Agent-based models of social behaviour and communication in evacuations: A systematic review

Most modern agent-based evacuation models involve interactions between evacuees. However, the assumed reasons for interactions and portrayal of them may be overly simple. Research from social psychology suggests that people interact and communicate with one another when evacuating and evacuee response is impacted by the way information is communicated. Thus, we conducted a systematic review of agent-based evacuation models to identify 1) how social interactions and communication approaches between agents are simulated, and 2) what key variables related to evacuation are addressed in these models. We searched Web of Science and ScienceDirect to identify articles that simulated information exchange between agents during evacuations, and social behaviour during evacuations. From the final 70 included articles, we categorised eight types of social interaction that increased in social complexity from collision avoidance to social influence based on strength of social connections with other agents. In the 17 models which simulated communication, we categorised four ways that agents communicate information: spatially through information trails or radii around agents, via social networks and via external communication. Finally, the variables either manipulated or measured in the models were categorised into the following groups: environmental condition, personal attributes of the agents, procedure, and source of information. We discuss promising directions for agent-based evacuation models to capture the effects of communication and group dynamics on evacuee behaviour. Moreover, we demonstrate how communication and group dynamics may impact the variables commonly used in agent-based evacuation models.Comment: Pre-print submitted to Safety Science special issue following the 2023 Pedestrian and Evacuation Dynamics conferenc

Multi-Agent Fitness Functions For Evolutionary Architecture

The dynamics of crowd movements are self-organising and often involve complex pattern formations. Although computational models have recently been developed, it is unclear how well their underlying methods capture local dynamics and longer-range aspects, such as evacuation. A major part of this thesis is devoted to an investigation of current methods, and where required, the development of alternatives. The main purpose is to utilise realistic models of pedestrian crowds in the design of fitness functions for an evolutionary approach to architectural design. We critically review the state-of-the-art in pedestrian and evacuation dynamics. The concept of 'Multi-Agent System' embraces a number of approaches, which together encompass important local and longer-range aspects. Early investigations focus on methods-cellular automata and attractor fields-designed to capture these respective levels. The assumption that pattern formations in crowds result from local processes is reflected in two dimensional cellular automata models, where mathematical rules operate in local neighbourhoods. We investigate an established cellular automata and show that lane-formation patterns are stable only in a low-valued density range. Above this range, such patterns suddenly randomise. By identifying and then constraining the source of this randomness, we are only able to achieve a small degree of improvement. Moreover, when we try to integrate the model with attractor fields, no useful behaviour is achieved, and much of the randomness persists. Investigations indicate that the unwanted randomness is associated with 2-lattice phase transitions, where local dynamics get invaded by giant-component clusters during the onset of lattice percolation. Through this in-depth investigation, the general limits to cellular automata are ascertained-these methods are not designed with lattice percolation properties in mind and resulting models depend, often critically, on arbitrarily chosen neighbourhoods. We embark on the development of new and more flexible methodologies. Rather than treating local and global dynamics as separate entities, we combine them. Our methods are responsive to percolation, and are designed around the following principles: 1) Inclusive search provides an optimal path between a pedestrian origin and destination. 2) Dynamic boundaries protect search and are based on percolation probabilities, calculated from local density regimes. In this way, more robust dynamics are achieved. Simultaneously, longer-range behaviours are also specified. 3) Network-level dynamics further relax the constraints of lattice percolation and allow a wider range of pedestrian interactions. Having defined our methods, we demonstrate their usefulness by applying them to lane-formation and evacuation scenarios. Results reproduce the general patterns found in real crowds. We then turn to evolution. This preliminary work is intended to motivate future research in the field of Evolutionary Architecture. We develop a genotype-phenotype mapping, which produces complex architectures, and demonstrate the use of a crowd-flow model in a phenotype-fitness mapping. We discuss results from evolutionary simulations, which suggest that obstacles may have some beneficial effect on crowd evacuation. We conclude with a summary, discussion of methodological limitations, and suggestions for future research

Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

Exitus: An Agent-Based Evacuation Simulation Model For Heterogeneous Populations

Evacuation planning for private-sector organizations is an important consideration given the continuing occurrence of both natural and human-caused disasters that inordinately affect them. Unfortunately, the traditional management approach that is focused on fire drills presents several practical challenges at the scale required for many organizations but especially those responsible for national critical infrastructure assets such as airports and sports arenas. In this research we developed Exitus, a comprehensive decision support system that may be used to simulate large-scale evacuations of such structures. The system is unique because it considers individuals with disabilities explicitly in terms of physical and psychological attributes. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions that have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency. The system was applied to three unique test beds: a multi-story office building, an international airport, and a major sports arena. Several simulation experiments revealed specific areas of concern for both building managers and management practice in general. In particular, we were able to show (a) how long evacuations of heterogeneous populations may be expected to last, (b) who the most vulnerable groups of people are, (c) the risk engendered from particular design features for individuals with disabilities, and (d) the potential benefits from adopting alternate evacuation strategies, among others. Considered together, the findings provide a useful foundation for the development of best practices and policies addressing the evacuation concerns surrounding heterogeneous populations in large, complex environments. Ultimately, a capabilities based approach featuring both tactical and strategic planning with an eye toward the unique problems presented by individuals with disabilities is recommended