Crowd Dynamics Research Articles

Pedestrian evacuation dynamics considering information guidance

Suitable information of routes and exits can help guide evacuees to approach the expected destinations more efficiently in case of emergencies, such as fire accidents, terrorist attacks and power failures. Therefore, the purpose of this paper is to provide an overview of the dynamics of two patterns of guided crowd evacuation. One is source-guided crowd evacuation, and the other is leader-guided crowd evacuation. In source-guided crowd evacuation, sources located in fixed positions diffuse information to evacuees around them in a certain form such as light, sound and smell. However, in the leader-guided crowd evacuation, leaders convey information to evacuees they encounter along their trajectories by means of word-of-mouth communication. First, considering wall-following, herding, and inertia behaviors, a mesoscopic model is established to formulate the movement of uninformed pedestrians without information guidance. Then, considering information guidance, a set of movement rules are constructed to describe the evolution dynamics of crowds comprising uninformed and informed pedestrians. Further, the implementation of the simulation models reveals the subtle effects of the initial position of information sources, the propagation velocity of information sources, and the initial distribution of leaders on the evacuation efficiency of crowds. This study gives new insights into the design of information guidance systems in pedestrian walking facilities.

System Identification for the design of behavioral controllers in crowd evacuations

Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distance-keeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input–output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures.

Data driven analysis of the desired speed in ordinary differential equation based pedestrian simulation models

In this paper, the desired speed is figured out as an important parameter for ordinary differential equation (ODE) based pedestrian simulation models. However, there lacks a thorough study on the optimal way of assigning the desired speed for ODE based pedestrian simulation models to achieve reliable performance. To gain a deep understanding of the role of the desired speed and its influence on the performances of the ODE based pedestrian simulation models, a total of nine assigning strategies of the desired speed are conducted a comparison study. Specifically, the performances of the ODE based models equipped with such desired speed assigning strategies are compared with regard to reference data from both the uni- and bi-directional pedestrian flow scenarios using a fair model comparison framework. Then, the performances are compared across several dimensions, including different assigning strategies of the desired speed, the different pedestrian simulation models, different experiments for the same motion base, and different motion base scenarios. Based on these results, four findings are summarized. Firstly, the desired speed is quantitatively verified to be an important role in the performance of ODE based pedestrian simulation models. Secondly, pedestrian density level is verified to be an effective indicator for the complexity of pedestrian crowd dynamics. Thirdly, the performance of the ODE based models is dependent on the extent to which the heterogeneity of desired speed is considered. Lastly, the performance of the social force model (SF) is suggested to be more predictable than optimal reciprocal collision avoidance model (ORCA) regarding the stability and optimality. Finally, based on the above findings, recommendations on assigning the desired speed for ODE based pedestrian simulation models are given.

Crowd Dynamics: Modeling and Control of Multiagent Systems

This review aims to present recent developments in modeling and control of multiagent systems. A particular focus is set on crowd dynamics characterized by complex interactions among agents, also called social interactions, and large-scale systems. Specifically, in a crowd each individual agent interacts with a field generated by the other agents and the environment. These systems can be modeled at the microscopic scale by ordinary differential equations, while an alternative description at the mesoscopic scale is given by a partial differential equation for the propagation of the probability density of the agents. Control actions can be applied at the individual level as well as at the level of the corresponding fields. This article presents and compares different control types, and the specific application to multilane, multiclass traffic is developed in some detail, showing the main tools at work in a hybrid setting with relevant impacts on autonomous driving.

Bayesian Modeling of Crowd Dynamics by Aggregating Multiresolution Observations From UAVs and UGVs

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) can be jointly deployed to form a collaborative surveillance system, where UAVs collect low-resolution images at a high altitude to obtain a global perception and UGVs observe high-resolution images within a focused detection range. Such multiresolution heterogeneous observations create opportunities yet pose challenges to model the dynamics of the targeted crowds. Existing approaches that integrate multiresolution observations rely on intensive computation of a large volume of historical data, and thus, result in a lack of computational efficiency, accuracy, and robustness. To address these limitations, this article proposes a new crowd dynamics modeling approach to aggregating the multiresolution information under a Bayesian inference framework. Beta-binomial and Normal–Wishart conjugate distributions were adopted to model crowd dynamics from low-resolution UAV observations and high-resolution UGV observations, respectively. Based on the proposed approach, a real-time model updating mechanism is developed to implement information aggregation for onboard crowd dynamics inference, where high efficiency, accuracy, and robustness are critical. Numerical simulations and onboard experiments were conducted to demonstrate the effectiveness and efficiency of the proposed modeling approach.

Dynamic Crowd Accident-Risk Assessment Based on Internal Energy and Information Entropy for Large-Scale Crowd Flow Considering COVID-19 Epidemic

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m–2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m–1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54% under this epidemic.

Active jamming of microswimmers at a bottleneck constriction

A study of the light-induced evacuation of a suspension of microwimmers (the micro-alga Chlamydomonas reinhardtii) through a bottleneck-shaped constriction is presented. We show that a transition from a jammed phase to an uninterrupted phase occurs when varying either the door size or the swimming velocity. The survival function of exit times is then found to evolve from a power law to an exponential trend. Moreover, we found that the evacuation time increases with increasing velocity, a behavior reminiscent of the ``faster-is-slower'' paradox present in crowd dynamics systems.

The crowd dynamics and collective stupidity of the January 6 riot: Theoretical analyses and prescriptions for a collectively wiser future.

The crowd dynamics and collective stupidity of the January 6 riot: Theoretical analyses and prescriptions for a collectively wiser future.

How people with disabilities influence crowd dynamics of pedestrian movement through bottlenecks

Despite considerable research efforts, most controlled empirical studies on crowd movement usually rely on homogeneous crowds, i.e., research participants are typically young adults without disabilities. Consequently, relatively little is known about pedestrian movement in more diverse and heterogeneous crowd conditions, e.g., when persons with reduced mobility are present. This gap may be particularly relevant at bottlenecks, along the path of a moving crowd, that limit the capacity of pedestrian flow. Here, we present results from 12 studies in which participants (total N = 252) with and without visible disabilities moved together in a crowd. In each study, groups of participants walked together in a hallway with a bottleneck at the end. The point of speed adoption, distances between neighbours, and behavioural activities were analysed. We found (1) that participants with disabilities reduced their speed further away from the bottleneck than participants without disabilities; (2) participants without disabilities stayed closer to neighbors with disabilities than to neighbors without disabilities; and (3) participants interacted and communicated with each other to organise in front of the bottleneck. These results underline the importance of studying representative and heterogeneous samples in crowd dynamics. We also argue that more interdisciplinary research is needed to better understand the dynamics of interactions between neighbors in a crowd. A more nuanced understanding of pedestrian dynamics holds the promise of improving the validity of simulation tools such as movement and evacuation models.

Pushing and Non-pushing Forward Motion in Crowds: A Systematic Psychological Observation Method for Rating Individual Behavior in Pedestrian Dynamics

Pushing behavior impairs people’s sense of well-being in a crowd and represents a significant safety risk. There are nevertheless still a lot of unanswered questions about who behaves how in a crowded situation, and when, where, and why pushing behavior occurs. Beginning from the supposition that a crowd is not thoroughly homogenous and that behavior can change over time, we developed a method to observe and rate forward motion. Based on the guidelines of quantitative content analysis, we came up with four categories: (1) falling behind, (2) just walking, (3) mild pushing, and (4) strong pushing. These categories allow for the classification of the behavior of any person at any time in a video, and thereby the method allows for a comprehensive systematization of individuals’ actions alongside temporal crowd dynamics. The application of this method involves videos of moving crowds including trajectories. The initial results show a very good inter-coder reliability between two trained raters demonstrating the general suitability of the system to describe forward motion in crowds systematically and quantify it for further analysis. In this way, pushing behavior can be better understood and, prospectively, risks better identified. This article offers a comprehensive presentation of this method of observation.

Crowd Dynamics and Co-Creation Practices In Robotics Innovation: An Interpretive Study Of Facilities

This paper aims to investigate the characteristics and organizing forms of facilities where the design and testing of robotics innovation happen. In particular, the paper addresses a gap in the state-of-the-art literature, which has not fully investigated the connection between those facilities, the different types of crowds involved in the design and testing of robotics innovation, and the values embedded in their outputs. To this end, this article presents a qualitative research based on two interpretive case studies of facilities and education initiatives in Italy and Switzerland. The article aims to provide a further understanding of the role of crowds in innovation, especially focusing on robotics, and to contribute to the stream of research connecting open innovation and resource-based view (RBV), by questioning how facilities for co-creation, specifically in robotics, inform the values that make both the testing sites and their outputs valuable resources. Also, the article adds its results to the corpus of studies on the collaboration between universities and firms, by highlighting organizational factors and trajectories that may eventually lead to the creation of innovation ecosystems and infrastructures.

Velocity-based dynamic crowd simulation by data-driven optimization

Velocity-based dynamic crowd simulation by data-driven optimization

Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows

In this paper, we present a novel approach for predicting pedestrian crowd dynamics over longer time horizons (30s). In dense environments over long time horizons, the number of pedestrian interactions is high, leading to the degradation of traditional pedestrian trajectory estimation techniques. Alternatively, we consider the macroscopic properties of the crowd as a whole, focusing on the flow of density. This approach benefits from not considering pedestrians individually, and can probabilistically estimate the existence of previously unobserved individuals. We propose a novel approach to imposing a physical constraint on the crowd density flow. Initially, a coarse resolution prediction is generated by a Convolutional Recurrent Neural Network (ConvRNN), and subsequently smoothly interpolated by a Gaussian Process (GP). Using the linearity properties of GPs, a continuous representation of the crowd is produced that complies with both the ConvRNN's prediction and a conservation of density constraint. The approach is trained and analysed on the dense ATC dataset, where we show the advantages of the approach and the improvements from our contributions.

Wisual: Indoor Crowd Density Estimation and Distribution Visualization Using Wi-Fi

Driven by the Internet of Things (IoT), many device-free crowd density estimation techniques can roughly estimate the crowd density based on the relationship between the dynamic crowd and the variation of wireless signals. However, they cannot distinguish the path information of different persons in a fine-grained manner. In this article, we propose Wisual, a channel state information (CSI)-based device-free crowd density estimation framework and can visualize the distribution of people. The major challenge of Wisual is how to extract proper quantifiable indexes to distinguish the path information of multiple targets and maximize the resolution of crowd density estimation. To address this challenge, Wisual first presents a method for estimating the frequency of the CSI propagation path (FoC) for the moving persons and constructs a joint multifeature parameter (JMFP) spectrum matrix with the other two parameters. Then the multitarget spectrum matrix is put into a proposed deep-learning model called CSI stream 3-D convolutional neural networks (CS-3DCNNs) for implementing crowd density estimation and the target path information differentiation. Finally, Wi-Fi imaging is implemented based on the 2-D-MUSIC algorithm, which shows the approximate distribution situation of indoor persons through the spectrograms. The experimental results in typical real-world scenes demonstrate that Wisual can forecast the crowd density with 98% precision and accurately display the frequency spectra of moving persons. Besides, the results also prove the superior effectiveness, scalability, and generalizability of the proposed framework.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: “Pedestrian crash frequency models”, “Pedestrian injury severity crash models”, “Traffic engineering measures in pedestrians’ safety”, “Global reports around pedestrian accident epidemiology”, “Effect of age and gender on pedestrians’ behavior”, “Distraction of pedestrians”, and “Pedestrian crowd dynamics and evacuation”. Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely “Collision avoidance and intelligent transportation systems (ITS)”, “Epidemiological studies of pedestrian injury and prevention”, “Pedestrian road crossing and behavioral factors”, “Pedestrian flow simulation”, and “Walkable environment and pedestrian safety”. Finally, “autonomous vehicle”, “pedestrian detection”, and “collision avoidance” themes were identified as having the greatest centrality and development degrees in recent years.

Modelling and simulating serpentine group behaviour in crowds using modified social force model

Crowd dynamics has emerged as an important topic of investigation in recent years. Several studies have been conducted to better understand the underlying factors that can influence crowd dynamics and lead to risky situations. Particularly, field studies are essential for realistically capturing the dynamics. This paper attempts to uncover one intriguing behaviour observed in the world’s largest mass religious gathering Kumbh Mela-2016, held in Ujjain, India. The authors define this behaviour, where groups attempt to overtake the others in the crowd, form flexible chains, and not allow non-group members to penetrate through them, as serpentine group behaviour. This behaviour has been modelled and simulated using a modified social force model which has been developed in this work. This can aid in the development of a realistic crowd simulation model that can handle such unique crowd behaviours and help manage large crowds and ensure safe movement.

An extended queueing model based on vision and morality for crowd evacuation

As one of the most common self-organizing phenomena in social systems, queueing has been attracting a lot of interest. Understanding the local interaction mechanism in queueing behaviors is a critical issue in the field of crowd dynamics. However, most studies ignore the heuristic decision during queueing processes. Even less is known about the effect of queueing behaviors on the collective movement. Therefore, an extended social force queueing model is proposed, which is based on vision and morality. Guided by visual and moral information, the desired speed and direction are heuristically adjusted in the self-driven force. Numerical simulations indicate that our model constructs a more realistic and detailed framework for the interpretation of pedestrian queueing. This model successfully predicts the emergence of the “detours” phenomenon, which is beneficial for reducing crowd pressure. Furthermore, our model may provide guidance for crowd management in safety science and navigation in autonomous robots.

Visual information based social force model for crowd evacuation

With the increase in large-scale incidents in real life, crowd evacuation plays a pivotal role in ensuring the safety of human crowds during emergency situations. The behavior patterns of crowds are well rendered by existing crowd dynamics models. However, most related studies ignore the information perception of pedestrians. To overcome this issue, we develop a visual information based social force model to simulate the interpretable evacuation process from the perspective of visual perception. Numerical experiments indicate that the evacuation efficiency and decision-making ability promote rapidly within a small range with the increase in unbalanced prior knowledge. The propagation of acceleration behavior caused by emergencies is asymmetric due to the anisotropy of visual information. Therefore, this model effectively characterizes the effect of visual information on crowd evacuation and provides new insights into the information perception of individuals in complex scenarios.

An attempt to distinguish physical and socio-psychological influences on pedestrian bottleneck.

It has been realized that the distinction between social-psychological effects and physical effects in pedestrian crowds is complex, and so the relevance of social psychology for the properties of pedestrian streams is still discussed controversially. Although physics-based models appear to capture many properties rather accurately, it was argued that simple systems of self-driven particles could not explain certain emergent phenomena. In particular, results from a recent empirical study of pedestrian flow at bottlenecks have been interpreted as indicating the relevance of social psychology even in relatively simple scenarios of crowd dynamics. The study showed a surprising dependence of the density near the bottleneck on the width of the corridor leading to it. The density increased with increasing corridor width, although a wider corridor provides more space for pedestrians. It has been argued that this observation is a consequence of social norms, which trigger the effect by a preference for queuing in such situations. However, convincing evidence for this hypothesis is still missing. Here, we reconsider this scenario from a physics perspective using computer simulations of a simple microscopic velocity-based model.

Waiting Behavior and Arousal in Different Levels of Crowd Density: A Psychological Experiment with a “Tiny Box”

Crowd density, defined as persons per square meter, is a basic measuring unit for describing and analyzing crowd dynamics and for planning pedestrian infrastructure. However, little is known about the relationship between crowd density and psychological stress and well-being. This study uses an experimental approach to determine whether higher crowd densities result in higher levels of stress in participants. In this experiment, which was a case study at the university, participants (N = 29) wait in a wooden box of 1 m2 for three minutes. Two, four, six, or eight participants are present simultaneously in the box. It is varied whether participants are supposed to remain silent or to speak with each other. Stress is conceptualized as arousal and measured as skin conductance level/electrodermal activity (EDA). A questionnaire is administered after the experiment, and the positioning of participants in the box is videotaped. The results show that the correlation between crowd density and physiological arousal is more complex than expected. The specific social situation in the box appears to play a more important role than merely the number of people waiting there. Furthermore, our data indicate a temporal trend: participants seem to adapt to the crowd density in the box. Video data analysis reveals that participants choose their positioning and orientation in the box carefully, but that this social choreography works less smoothly in higher densities. This study shows promising results for using EDA as a measurement of arousal in the context of crowd research. However, the limitations of this method and the experiments conducted are also discussed in detail to further improve this approach.