Panic, irrationality, herding: Three ambiguous terms in crowd dynamics research

Background: The three terms “panic”, “irrationality” and “herding” are ubiquitous in the crowd dynamics literature and have a strong influence on both modelling and management practices. The terms are also commonly shared between the scientific and non-scientific domains. The pervasiveness of the use of these terms is to the point where their underlying assumptions have often been treated as common knowledge by both experts and lay persons. Yet, at the same time, the literature on crowd dynamics presents ample debate, contradiction and inconsistency on these topics. Method: This review is the first to systematically revisit these three terms in a unified study to highlight the scope of this debate. We extracted from peer-reviewed journal articles direct quotes that offer a definition, conceptualisation or supporting/contradicting evidence on these terms and/or their underlying theories. To further examine the suitability of the term herding, a secondary and more detailed analysis is also conducted on studies that have specifically investigated this phenomenon in empirical settings. Results. The review shows that (i) there is no consensus on the definition for the terms panic and irrationality; and that (ii) the literature is highly divided along discipline lines on how accurate these theories/terminologies are for describing human escape behaviour. The review reveals a complete division and disconnection between studies published by social scientists and those from the physical science domain; also, between studies whose main focus is on numerical simulation versus those with empirical focus. (iii) Despite the ambiguity of the definitions and the missing consensus in the literature, these terms are still increasingly and persistently mentioned in crowd evacuation studies. (iv) Different to panic and irrationality, there is relative consistency in definitions of the term herding, with the term usually being associated with ‘(blind) imitation’. However, based on the findings of empirical studies, we argue why, despite the relative consistency in meaning, (v) the term herding itself lacks adequate nuance and accuracy for describing the role of ‘social influence’ in escape behaviour. Our conclusions also emphasise the importance of distinguishing between the social influence on various aspects of evacuation behaviour and avoiding generalisation across various behavioural layers. Conclusions. We argue that the use of these three terms in the scientific literature does not contribute constructively to extending the knowledge or to improving the modelling capabilities in the field of crowd dynamics. This is largely due to the ambiguity of these terms, the overly simplistic nature of their assumptions, or the fact that the theories they represent are not readily verifiable. Recommendations: We suggest that it would be beneficial for advancing this research field that the phenomena related to these three terms are clearly defined by more tangible and quantifiable terms and be formulated as verifiable hypotheses, so they can be operationalised for empirical testing

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

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees? safety and overall experience. Single-Input-SingleOutput (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input?output MISO models, linear and neural, are identified using a datadriven approach in which input?output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models? structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenariosUAH-Catedra MANED

CellEVAC: an adaptive guidance system for crowd evacuation through behavioral optimization

A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cellbased pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation?optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%).Ministerio de Economía y Competitivida

Development of a Dynamical Egress Behavioural Model under Building Fire Emergency

Building fire accidents, as a continuing menace to the society, not only incur enormous property damage but also pose significant threats to human lives. More recently, driven by the rapid population growth, an increasing number of large-capacity buildings are being built to meet the growing residence demands in many major cities globally, such as Sydney, Hong Kong, London, etc. These modern buildings usually have complex architectural layouts, high-density occupancy settings, which are often filled with a variety of flammable materials and items (i.e., electrical devices, flammable cladding panels etc.). For such reasons, in case of fire accidents, occupants of these buildings are likely to suffer from an extended evacuation time. Moreover, in some extreme cases, occupants may have to escape through a smoke-filled environment. Thus, having well-planned evacuation strategies and fire safety systems in place is critical for upholding life safety. Over the last few decades, due to the rapid development in computing power and modelling techniques, various numerical simulation models have been developed and applied to investigate the building evacuation dynamics under fire emergencies. Most of these numerical models can provide a series of estimations regarding building evacuation performance, such as predicting building evacuation time, visualising evacuation dynamics, identifying high-density areas within the building etc. Nevertheless, the behavioural variations of evacuees are usually overlooked in a significant proportion of such simulations. Noticeably, evacuees frequently adjust their egress behaviours based on their internal psychological state (i.e., the variation of stress) and external stimulus from their surrounding environments (i.e., dynamical fire effluents, such as high-temperature smoke). Evidence suggests that evacuees are likely to shift from a low-stress state to a high-stress state and increase their moving speed when escaping from a high-temperature and smoke-filled environment. Besides, competitive behaviours can even be triggered under certain extremely stressful conditions, which can cause clogging at exits or even stampede accidents. Without considering such behavioural aspects of evacuees, the predicted evacuation performance might be misinterpreted based on unreliable results; thereby, misleading building fire safety designs and emergency precautions. Therefore, to achieve a more realistic simulation of building fire evacuation processes, this research aims to advance in modelling of human dynamical behaviour responses of each evacuee and integrating it into building fire evacuation analysis. A dynamical egress behaviour-based evacuation model that considering the evacuee’s competitive/cooperative egress movements and their psychological stress variation is developed. Furthermore, a fire hazard-integrated evacuation simulation framework is established by coupling with the fire dynamics simulator (i.e., FDS). By means of tracking dynamical interactions between evacuees and the evolutionary fire dynamics within the building space, evacuees’ local fire risks and stress levels under the impacts of locally encountered fire hazards (i.e., radiation, temperature, toxic gas, and visibility) can be effectively quantified. In this study, the developed simulation tool can provide a further in-depth building fire safety assessment. Thus, it contributes to performance-based fire safety engineering in designs and real applications, including reducing budgets and risks of participating in evacuation drills, supporting emergency evacuation strategy planning, mitigating fire risks by identifying risk-prone areas associated with building fire circumstances (e.g., putting preventative measures in place beforehand to intervene or mitigate safety risks, such as mass panic, stampede, stress evoked behaviours)

Adaptive cell-based evacuation systems for leader-follower crowd evacuation

The challenge of controlling crowd movement at large events expands not only to the realm of emergency evacuations but also to improving non-critical conditions related to operational efficiency and comfort. In both cases, it becomes necessary to develop adaptive crowd motion control systems. In particular, adaptive cell-based crowd evacuation systems dynamically generate exit-choice recommendations favoring a coordinated group dynamic that improves safety and evacuation time. We investigate the viability of using this mechanism to develop a ‘‘leader-follower’’ evacuation system in which a trained evacuation staff guides evacuees safely to the exit gates. To validate the proposal, we use a simulation–optimization framework integrating microscopic simulation. Evacuees’ behavior has been modeled using a three-layered architecture that includes eligibility, exit-choice changing, and exit-choice models, calibrated with hypothetical-choice experiments. As a significant contribution of this work, the proposed behavior models capture the influence of leaders on evacuees, which is translated into exitchoice decisions and the adaptation of speed. This influence can be easily modulated to evaluate the evacuation efficiency under different evacuation scenarios and evacuees’ behavior profiles. When measuring the efficiency of the evacuation processes, particular attention has been paid to safety by using pedestrian Macroscopic Fundamental Diagrams (p-MFD), which model the crowd movement dynamics from a macroscopic perspective. The spatiotemporal view of the evacuation performance in the form of crowd-pressure vs. density values allowed us to evaluate and compare safety in different evacuation scenarios reasonably and consistently. Experimental results confirm the viability of using adaptive cell-based crowd evacuation systems as a guidance tool to be used by evacuation staff to guide evacuees. Interestingly, we found that evacuation staff motion speed plays a crucial role in balancing egress time and safety. Thus, it is expected that by instructing evacuation staff to move at a predefined speed, we can reach the desired balance between evacuation time, accident probability, and comfort

Developing novel optimization and machine learning frameworks to improve and assess the safety of workplaces

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model\u27s robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace\u27s safety under an active shooter incident

Modeling Family Behaviors in Crowd Simulation

Modeling human behavior for a general situation is difficult, if not impossible. Crowd simulation represents one of the approaches most commonly used to model such behavior. It is mainly concerned with modeling the different human structures incorporated in a crowd. These structures could comprise individuals, groups, friends, and families. Various instances of these structures and their corresponding behaviors are modeled to predict crowd responses under certain circumstances and to subsequently improve event management, facility and emergency planning. Most currently existing modeled behaviors are concerned with depicting individuals as autonomous agents or groups of agents in certain environments. This research focuses on providing structural and state-based behavioral models for the concept of a family incorporated in the crowd. The structural model defines parents, teenagers, children, and elderly as members of the family. It also draws on the associated interrelationships and the rules that govern them. The behavioral model of the family encompasses a number of behavioral models associated with the triggering of certain well-known activities that correspond to the family’s situation. For instance, in normal cases, a family member(s) may be hungry, bored, or tired, may need a restroom, etc. In an emergency case, a family may experience the loss of a family member(s), the need to assist in safe evacuation, etc. Activities that such cases trigger include splitting, joining, carrying children, looking for family member(s), or waiting for them. The proposed family model is implemented on top of the RVO2 library that is using agent-based approach in crowd simulation. Simulation case studies are developed to answer research questions related to various family evacuation approaches in emergency situations