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Simulating the Dynamic Escape Process in Large Public Places

Author

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  • Ziyou Gao

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Yunchao Qu

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xingang Li

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Jiancheng Long

    (School of Transportation Engineering, Hefei University of Technology, Hefei 230009, China)

  • Hai-Jun Huang

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

Pedestrian dynamics plays an important role in public facility design and evacuation management. During an escape process from a large public space, crowd behavior is a collection of pedestrian exit/route choice behavior, and movement behavior. Modelling such an escape process is an extremely complex challenge. In this paper, an integrated macro-micro approach is developed to simulate the escape process. An analysis of the simulation reveals the mechanisms of the formation of crowd congestion and flow distribution. At the macroscopic level, a mathematical model, based on the concept of the dynamic user optimal (DUO) criterion, is formulated to describe the pedestrian exit/route choice behavior. A method based on the fundamental diagram and point-queuing theory is developed to estimate the pedestrian escape time. At the microscopic level, a modified social force model is adopted to formulate pedestrians' dynamic movements during the escape process. A solution algorithm is proposed to solve the macro-micro integrated model and a series of experiments are carried out to validate the proposed model. The simulation results agree with the extracted experimental data. Finally, the integrated model and algorithm are used to simulate the escape process in a large public place. The proposed approach is able to generate the bandwagon effect, bottleneck effect, and route choice patterns.

Suggested Citation

  • Ziyou Gao & Yunchao Qu & Xingang Li & Jiancheng Long & Hai-Jun Huang, 2014. "Simulating the Dynamic Escape Process in Large Public Places," Operations Research, INFORMS, vol. 62(6), pages 1344-1357, December.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:6:p:1344-1357
    DOI: 10.1287/opre.2014.1312
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    2. Huang, Hai-Jun & Xia, Tian & Tian, Qiong & Liu, Tian-Liang & Wang, Chenlan & Li, Daqing, 2020. "Transportation issues in developing China's urban agglomerations," Transport Policy, Elsevier, vol. 85(C), pages 1-22.
    3. Xiao, Yao & Yang, Mofeng & Zhu, Zheng & Yang, Hai & Zhang, Lei & Ghader, Sepehr, 2021. "Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach," Transport Policy, Elsevier, vol. 109(C), pages 12-23.
    4. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    5. Ji, Xiangfeng & Zhang, Jian & Hu, Yongkai & Ran, Bin, 2016. "Pedestrian movement analysis in transfer station corridor: Velocity-based and acceleration-based," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 416-434.
    6. Xiaoge Zhang & Sankaran Mahadevan & Kai Goebel, 2019. "Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 2054-2075, September.

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