Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition—mainly family groups composed of members with differing mobility levels—plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds

Modelling and simulation of rail passengers to evaluate methods to reduce dwell times

The paper outlines a feasibility study using modelling and simulation to reduce dwell times and increase rail network capacity. We use agent based modelling, where passengers are treated as a separate entities, basing their movements on rules from the Social Force Model (SFM), proposed by Helbing to model pedestrian dynamics. Implementing this SFM, together with a novel decision making system for passengers' door choices, a mesoscopic model is produced of the platform, train and passengers. An outline of the modelling process is presented, along with a critical analysis of the final model. Analyses are conducted to evaluate novel concepts in train and platform design, to reduce loading times, using passengers with a range of attributes. In a simulation experiment, four concepts (wider doors, designated boarding/alighting doors, and an active passenger information system) are assessed, with the latter two giving reductions in loading times of 7.0% and 7.3%

Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective

Due to the highly developed rail transit over the past decades, the phenomena of complex individual self-organized behaviors and mass crowd dynamics have become a great concern in the train station. In order to understand passengers&rsquo walking-edge effect and analyze the relationship between the layout and sustainable service abilities of the train station, a heuristics-based social force model is proposed to elaborate the crowd dynamics. Several evacuation scenarios are implemented to describe the walking-edge effect in a train station with the evacuation efficiency, pedestrian flow, and crowd density map. The results show that decentralizing crowd flow can significantly increase the evacuation efficiency in different scenarios. When the exits are far away from the central axis of the railway station, the walking-edge effect has little influence on the evacuation efficiency. Obstacles can guide the movement of passengers by channelizing pedestrian flows. In addition, a wider side exit of the funnel-shaped corridors can promote walking-edge effect and decrease the pressure among a congested crowd. Besides providing a modified social force model with considering walking-edge effect, several suggestions are put forward for managers and architects of the train station in designing sustainable layouts. Document type: Articl

Urban tourism crowding dynamics: Carrying capacity and digital twinning

The increase in tourism activity globally has led to overcrowding, causing damage to local ecosystems and degradation of the tourism experience. To plan tourist activity it is necessary to define adequate indicators and understand the dynamics of tourist crowds. The main goals of this dissertation are the development of (1) an algorithm for assessing spatially fine-grained, physical carrying capacity (PCC) for a complex urban fabric, (2) an agent-based simulation model for the egress of participants in public open space tourism attraction events and (3) an agent-based simulation model using the PCC algorithm for tourism crowding stress analysis in urban fabric constrained scenarios. OpenStreetMap open-data was used throughout this research. The proposed PCC algorithm was tested in Santa Maria Maior parish in Lisbon that has a complex ancient urban fabric. The GAMA agent-based platform was used in the two simulation studies. The first compared two scenarios (normal and COVID-19) in three major public spaces in Lisbon and the second focused on the simulation of a real-time tourism crowding stress analysis scenario of visitors’ arrival at the Lisbon Cruise Terminal. The results show the proposed algorithm’s feasibility to determine the PCC of complex urban fabrics zones and its application as an initial reference value for the evaluation of real-time crowding stress, namely in simulations for assessing overtourism scenarios, both in public open spaces as in highly constrained urban fabrics.O aumento da atividade turística a nível global tem levado à superlotação, causando danos aos ecossistemas locais e degradação da experiência turística. Para planear a atividade turística é necessário definir indicadores adequados e entender as dinâmicas das multidões turísticas. Os principais objetivos desta dissertação são o desenvolvimento de (1) um algoritmo para avaliar a capacidade de carga física (CCF) de fino grão espacial para uma malha urbana complexa, (2) um modelo de simulação baseado em agentes para o escoamento de participantes em eventos de atração turística em espaços abertos e (3) um modelo de simulação baseado em agentes usando o algoritmo de CCF para análise do stress de aglomeração de turistas em cenários de malha urbana restritiva. Os dados abertos do OpenStreetMap foram usados nesta investigação. O algoritmo CCF proposto foi testado na freguesia de Santa Maria Maior, em Lisboa, que tem uma malha urbana antiga e complexo. A plataforma GAMA baseada em agentes foi usada nos dois estudos de simulação. O primeiro comparou dois cenários (normal e COVID-19) em três grandes espaços públicos de Lisboa e o segundo analisou o stress de aglomeração causado pela chegada de navios ao Terminal de Cruzeiros de Lisboa. Os resultados mostram a viabilidade do algoritmo proposto para determinar a CCF de zonas com tecidos urbanos complexos e a sua aplicação como valor de referência inicial para a avaliação do stress de superlotação em tempo real, nomeadamente na avaliação de cenários de aglomeração turística excessiva, tanto em espaços abertos, como em malhas urbanas intrincadas

Computational Study of Social Interactions and Collective Behavior During Human Emergency Egress.

Egress of occupants from a facility is normally straightforward. Problems arise when an emergency is present and many occupants are attempting to egress as quickly as possible, at which point egress can become life threatening. There are many reported events in history where emergency egress resulted in extensive loss of life and injuries. Egress research depends heavily on computational modeling because ethical and safety concerns preclude running experiments involving emergency crowd evacuations. However, to date, existing egress models rarely take into account meaningful social interactions and adherence to cultural norms, both of which are commonly present among egressing occupants and have significant influence on their egress response. The objective of this study is to develop a new methodology to address this gap using an Agent-Based computational platform. A novel method, termed Scalar Field Method (SFM), is proposed to accomplish this goal. The new technique draws on an analogy to a charged particle in an electromagnetic field to simulate the decision making process of an agent as it navigates through a facility and considers social interactions in its quest to egress. Two categories of social interactions are accounted for: 1) pre-existing social relationships associated with social identities, and 2) informal relations in collective behaviors such as lining up in counter-flow, queuing, and collective mobility. The latter is achieved by requiring an agent to establish informal and transient leader-follower relationships with others while adjusting its behavioral patterns as warranted by the situation. Simulation results demonstrate the model’s capabilities of handling social interactions, modeling reasonable egress behavior, and mimicking self-organized social gathering and collective behavior during egress. Comparisons with field studies show that the computational results correlate realistically with experimental data. A case study of the Station Nightclub fire that occurred in Rhode Island in 2003 and killed 100 occupants demonstrates that the proposed computational tools have strong potential for quantitatively exploring the influence of social level traits on egress situations.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113381/1/calcite_1.pd

Guide them through: an automatic crowd control framework using multi-objective genetic programming

We propose an automatic crowd control framework based on multi-objective optimisa- tion of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quanti- tatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path

Speed modulated social influence in evacuating pedestrian crowds

Evacuation is a complex social phenomenon with individuals tending to exit a confined space as soon as possible. Social factors that influence an individual include collision avoidance and conformity with others with respect to the tendency to exit. While collision avoidance has been heavily focused on by the agent-based models used frequently to simulate evacuation scenarios, these models typically assume that all agents have an equal desire to exit the scene in a given situation. It is more likely that, out of those who are exiting, some are patient while others seek to exit as soon as possible. Here, we experimentally investigate the effect of different proportions of patient (no-rush) versus impatient (rush) individuals in an evacuating crowd of up to 24 people. Our results show that a) average speed changes significantly for individuals who otherwise tended to rush (or not rush) with both type of individuals speeding up in the presence of the other; and b) deviation rate, defined as the amount of turning, changes significantly for the rush individuals in the presence of no-rush individuals. We then seek to replicate this effect with Helbing's social force model with the twin purposes of analyzing how well the model fits experimental data, and explaining the differences in speed in terms of model parameters. We find that we must change the interaction parameters for both rush and no-rush agents depending on the condition that we are modeling in order to fit the model to the experimental data