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Modeling detour behavior of pedestrian dynamics under different conditions

Author

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  • Qu, Yunchao
  • Xiao, Yao
  • Wu, Jianjun
  • Tang, Tao
  • Gao, Ziyou

Abstract

Pedestrian simulation approach has been widely used to reveal the human behavior and evaluate the performance of crowd evacuation. In the existing pedestrian simulation models, the social force model is capable of predicting many collective phenomena. Detour behavior occurs in many cases, and the important behavior is a dominate factor of the crowd evacuation efficiency. However, limited attention has been attracted for analyzing and modeling the characteristics of detour behavior. In this paper, a modified social force model integrated by Voronoi diagram is proposed to calculate the detour direction and preferred velocity. Besides, with the consideration of locations and velocities of neighbor pedestrians, a Logit-based choice model is built to describe the detour direction choice. The proposed model is applied to analyze pedestrian dynamics in a corridor scenario with either unidirectional or bidirectional flow, and a building scenario in real-world. Simulation results show that the modified social force model including detour behavior could reduce the frequency of collision and deadlock, increase the average speed of the crowd, and predict more practical crowd dynamics with detour behavior. This model can also be potentially applied to understand the pedestrian dynamics and design emergent management strategies for crowd evacuations.

Suggested Citation

  • Qu, Yunchao & Xiao, Yao & Wu, Jianjun & Tang, Tao & Gao, Ziyou, 2018. "Modeling detour behavior of pedestrian dynamics under different conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1153-1167.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1153-1167
    DOI: 10.1016/j.physa.2017.11.044
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    Citations

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    Cited by:

    1. Jin, Cheng-Jie & Shi, Ke-Da & Jiang, Rui & Li, Dawei & Fang, Shuyi, 2023. "Simulation of bi-directional pedestrian flow under high densities using a modified social force model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Lee, Minhyuck & Lee, Jaeyoung & Jun, Chulmin, 2021. "An extended floor field model considering the spread of fire and detour behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    3. Zheng, Linjiang & Peng, Xiaoli & Wang, Linglin & Sun, Dihua, 2019. "Simulation of pedestrian evacuation considering emergency spread and pedestrian panic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 167-181.
    4. Cheng-Jie Jin & Ke-Da Shi & Shu-Yi Fang, 2023. "Simulation of Single-File Pedestrian Flow under High-Density Condition by a Modified Social Force Model," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    5. Huang, Di & Yang, Yuwei & Peng, Xinyi & Huang, Jiangyan & Mo, Pengli & Liu, Zhiyuan & Wang, Shuaian, 2024. "Modelling the pedestrian’s willingness to walk on the subway platform: A novel approach to analyze in-vehicle crowd congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    6. Zheng, Ying & Li, Xingang & Zhu, Nuo & Jia, Bin & Jiang, Rui, 2018. "Evacuation dynamics with smoking diffusion in three dimension based on an extended Floor-Field model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 414-426.
    7. Tang, Tie-Qiao & Zhang, Bo-Tao & Zhang, Jian & Wang, Tao, 2019. "Statistical analysis and modeling of pedestrian flow in university canteen during peak period," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 29-40.
    8. Zhou, Min & Ge, Shichao & Liu, Jiali & Dong, Hairong & Wang, Fei-Yue, 2020. "Field observation and analysis of waiting passengers at subway platform — A case study of Beijing subway stations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    9. Shi Sun & Cheng Sun & Dorine C. Duives & Serge P. Hoogendoorn, 2023. "Neural network model for predicting variation in walking dynamics of pedestrians in social groups," Transportation, Springer, vol. 50(3), pages 837-868, June.
    10. Qingyan Ning & Maosheng Li, 2022. "Modeling Pedestrian Detour Behavior By-Passing Conflict Areas," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    11. Li, Maosheng & Shu, Panpan & Xiao, Yao & Wang, Pu, 2021. "Modeling detour decision combined the tactical and operational layer based on perceived density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).

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