Simulation of pedestrian evacuation route choice using social force model in large-scale public space: Comparison of five evacuation strategies

Zhou, Jibiao; Guo, Yanyong; Dong, Sheng; Zhang, Minjie; Mao, Tianqi

doi:10.1371/journal.pone.0221872

Cited by 23 publications

(15 citation statements)

References 41 publications

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“…Simulation and recommendation of evacuation methods is a type of route recommendation where public spaces are considered [8]. Such types of researches help design evacuation strategies to reduce casualties and damages in the events of natural or man-made disasters.…”

Section: A Route Recommendationmentioning

confidence: 99%

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

et al. 2022

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The recent increase in sedentary lifestyles has highlighted the importance of physical activity in our daily lives. Although fall accidents are a common cause of injuries among the elderly, walking is one of the simplest physical activities available. Therefore, walking route selection and identification of walking behavior in different scenarios remains a widely researched area. This paper is about developing a walking-route recommender system that proposes a safe route to the user and reduces the probability of fall accidents. We determine route candidates after the start and endpoints are fed to the system. Then the tripping risk involved in each route candidate is identified by performing several walking simulations of a digital human model on a three-dimensional laser-scanned point cloud. Finally, a safe route is determined after considering the user's age and the tripping risk. It was confirmed that tripping risk could be competently estimated according to the considered age group.

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Section: A Route Recommendationmentioning

confidence: 99%

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

et al. 2022

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“…Above mentioned studies have mainly dealt with the development of descriptive models of evacuation behavior that simulate the real movement of crowds (Duives et al, 2013;Ronchi et al, 2016). These models allow studying the influence of different behavior strategies on evacuation performance (Zhou et al, 2019). However, behavioral strategy optimization is neglected to great degrees (Berseth et al, 2015;Noh et al, 2016;Ding et al, 2017).…”

Section: Crowd Behavior Modelingmentioning

confidence: 99%

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

Lopez-Carmona,

Garcia

2020

Preprint

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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 cell-based 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%).

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“…A basic assumption of the GHM model is that the driver decides whether to stop or pass the intersection according to the relationship between the maximum passing distance and the minimum stopping distance at the beginning of the yellow light. Several notable variants have also been reported in the literature [22][23][24][25]. e GHM model assumes that all drivers will choose to stop, if possible.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

Dong

Zhou

2020

Journal of Advanced Transportation

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The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.

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Simulation of pedestrian evacuation route choice using social force model in large-scale public space: Comparison of five evacuation strategies

Cited by 23 publications

References 41 publications

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

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

A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

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