Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with...
Contexts in source publication
... effect, the height along the surface of the cone encodes the distance to the center of the entity. When rendered from an orthographic top view facing the tips (free of any perspective distortion), two cones will overlap at precise points that are equidistant to both entities, as seen in Figure 3. ...
... Mobility model: these papers present an innovation in terms of mobility modeling. Two papers in this category (Prédhumeau et al. 2021, Hesham andWainer 2021) deal with pedestrian mobility: their objective is to propose an improved model to simulate pedestrian behavior. A third paper, i.e. ...
At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phúc Xá district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network.
... Most crowd models are based on assumptions, intuition and literature and many reflect specific scenarios (Dubroca-Voisin et al., 2019;Li et al., 2015). Crowd modelling involves simulating real-world crowds based on scientific hypotheses related to social, physical, biological and psychological factors (Bellomo, Clarke, Gibelli, Townsend, & Vreugdenhil, 2016;Hesham & Wainer, 2021;Zhang et al., 2018). For example, when looking at a crowd as a collective movement and studying its behaviour as a physical (e.g., fluid) or biological (e.g., animal swarm) phenomena including basic interactions, crowd models can describe real-world events using mathematical processes. ...
Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by implementing efficient crowd learning models that can anticipate critical crowd conditions and potential catastrophes. Developing an SCM solution involves monitoring crowds and modelling their dynamics. Crowd monitoring produces vast amounts of data, with features such as densities and speeds, which are essential for training and evaluating crowd learning models. By and large, crowd datasets can be classified as real (e.g., real monitoring of crowds) or synthetic (e.g., simulation of crowds). Using real crowd datasets can produce effective and reliable crowd learning models. However, acquiring real crowd data faces several challenges, including the expensive installation of a sensory infrastructure, the data pre-processing costs and the lack of real datasets that cover particular crowd scenarios. Consequently, crowd management literature has adopted simulation tools for generating synthetic datasets to overcome the challenges associated with their real counterparts. The majority of existing datasets, whether real or synthetic, can be used for crowd counting applications or analysing the activities of individuals rather than collective crowd behaviour. Accordingly, this paper demonstrates the process of generating bespoke synthetic crowd datasets that can be used for crowd anomaly detection and prediction, using the MassMotion crowd simulator. The developed datasets present two types of crowd anomalies; namely, high densities and contra-flow walking direction. These datasets are: SIMulated Crowd Data (SIMCD)-Single Anomaly and SIMCD-Multiple Anomalies for anomaly detection tasks, besides two SIMCD-Prediction datasets for crowd prediction tasks. Furthermore, the paper demonstrates the data preparation (pre-processing) process by aggregating the data and proposing new essential features, such as the level of crowdedness and the crowd severity level, that are useful for developing crowd prediction and anomaly detection models.
... In recent years, scholars have conducted extensive and indepth research on crowd evacuation.  For example, for the evacuation of dense crowds, Hesham and Wainer 1 presented advanced models based on centroidal particle dynamics (CPD), an agent-based short-range collisionavoidance model for pedestrians in dense crowds. Their models can reproduce visually convincing emergent crowd phenomena. ...
In China, training schools are ubiquitous, where heterogeneous pedestrian flow (which consists of adults and children) widely occurs during the after-class period. In this paper, we develop a fine grid cellular CA model to describe the pedestrian behaviors (e.g., pick-up behavior, searching behavior, matching behavior, waiting behavior, leading behavior, and following behavior) at a training school during the after-class period and explore the effects of the special behaviors on each pedestrian’s movement in and around a classroom. To describe the heterogeneous pedestrian flow accurately, (i) some questionnaire surveys are designed to extract some features of adult’s and child’s movement, and (ii) some video experiments are conducted to estimate/calibrate some parameters of interest in the proposed model. Finally, some strategies are designed to enhance the evacuation efficiency and the operational efficiency of training school. The numerical results indicate that the proposed model can reasonably match with reality, and the proposed strategies can enhance the evacuation efficiency and the operational efficiency of training school. The results can help the administrators to effectively manage the pedestrian evacuation at training school during the after-class period.
The visual attention of pedestrians has been rarely considered in studies of congestion prevention in long-distance passages. This paper proposes a kinetic theory model of human crowds accounting for visual attention to study congestion in long-distance passages. The population is divided into visual attention-shifting pedestrians (VAS pedestrians) and nonvisual attention-shifting pedestrians (non-VAS pedestrians). First, the movement characteristics of all pedestrians are analyzed based on observations and measurements obtained through controlled experiments. Moreover, a pedestrian flow model accounting for visual attention is built to transform the characteristics of pedestrian movement into a mathematical model. Finally, validation is done, and the density and the proportion of VAS pedestrians are selected as congestion warning parameters. Simulations are performed for a subway passage connected to stairs, and the effect of visual attention, the critical thresholds of congestion warning parameters, and the effects of implementing mitigation measures immediately after congestion occurs are assessed. The experimental results show that the movement characteristics of VAS pedestrians and non-VAS pedestrians are different. Simulation results show that the model is effective. Notably, visual attention has an impact on pedestrian movement, and using the density and the proportion of VAS pedestrians as early warning indicators is effective for preventing the occurrence of congestion, as demonstrated by the negative correlation between the two critical thresholds. This description of human groups provides quantitative guidelines for crowd management.
The emergency response process will not start immediately in the initial stage of a subway accident, and information will not fully spread temporarily. Crowds in different regions receive different comprehensiveness of accident information. They will start evacuating at different times and at different desired speed states. Depending on this phenomenon, pedestrians were classified in simulation modeling. In addition, the different states of emergency gates, escalators, and automatic gate machines before and after the emergency response instruction were set up in the simulation. The impact of different emergency response times on evacuations was studied. The emergency response in advance did not make a significant promotion to evacuation efficiency, but delayed emergency response reduced it greatly. This work proposed an evacuation risk index (ERI) and maximum safe evacuation capacity in metro stations. The worst accident evacuation scenario can be obtained by calculating the ERI, and a metro station was evaluated as a case study. The comparison of the evaluation results of ERI and other methods shows that the conclusion is consistent, but ERI considers the accident location factor.
Crowd dynamics have constituted a hotspot of research in recent times, particularly in areas where developmental progress has taken place in crowd evacuation for ensuring human safety. In high-density crowd events which happen frequently, panic or an emergency can lead to an increase in congestion which may cause disastrous incidents. Crowd control planning via simulation of people’s movement and behavior can promote safe departures from a space, despite threatening circumstances. Up until now, the evolution of distinctive types of crowd behavior towards cooperative flow remains unexplored. Hence, in this paper, we investigate the impact of potential crowd behavior, namely best-response, risk-seeking, risk-averse, and risk-neutral agents in achieving cooperation during evacuation and its connection with evacuation time using a game-theoretic evacuation simulation model. We analyze the crowd evacuation of a rectangular room with either a single-door or multiple exits in a continuous space. Simulation results show that mutual cooperation during evacuation can be realized when the agents’ population is dominated by risk-averse agents. The results also demonstrate that the risk-seeking agents tend toward aggressiveness by opting for a defector strategy regardless of the local crowd densities, while other crowd behavior shows cooperation under high local crowd density.
Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.
Crowd simulation demands careful consideration in regard to the classic trade-off between accuracy and efficiency. Particle-based methods have seen success in various applications in architecture, military, urban planning, and entertainment. This method focuses on local dynamics of individuals in large crowds, with a focus on serious games and entertainment. The technique uses an area-based penalty force that captures the infringement of each entity's personal space. This method does not need a costly nearest-neighbor search and allows for an inherently data-parallel implementation capable of simulating thousands of entities at interactive frame rates. The algorithm reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.