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Dynamic stride length adaptation according to utility and personal space

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  • von Sivers, Isabella
  • Köster, Gerta

Abstract

Pedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They try to avoid collisions and to preserve their personal space. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the pedestrians could go. The stride length and its correct adaptation, however, are rarely considered. This leads to artefacts that impact macroscopic observation parameters such as densities in front of bottlenecks and, through this, flow. Hence modelling stride adaptation is important to increase the predictive power of pedestrian models. To achieve this we reformulate the problem as an optimisation problem on a disk around the pedestrian. Each pedestrian seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need for individual space and the need to keep a distance from obstacles. The need for space is modelled according to findings from psychology defining zones around a person that, when invaded, cause unease. The result is a fully automatic adjustment that allows calibration through meaningful social parameters and that gives visually natural results with an excellent fit to measured experimental data.

Suggested Citation

  • von Sivers, Isabella & Köster, Gerta, 2015. "Dynamic stride length adaptation according to utility and personal space," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 104-117.
  • Handle: RePEc:eee:transb:v:74:y:2015:i:c:p:104-117
    DOI: 10.1016/j.trb.2015.01.009
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    4. Wang, Jiayue & Boltes, Maik & Seyfried, Armin & Zhang, Jun & Ziemer, Verena & Weng, Wenguo, 2018. "Linking pedestrian flow characteristics with stepping locomotion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 106-120.
    5. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani, 2016. "A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 159-176.
    6. Zeng, Guang & Cao, Shuchao & Liu, Chi & Song, Weiguo, 2018. "Experimental and modeling study on relation of pedestrian step length and frequency under different headways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 237-248.
    7. Huang, Zhongyi & Chraibi, Mohcine & Cao, Shuchao & Huang, Chuanli & Fang, Zhiming & Song, Weiguo, 2019. "A microscopic method for the evaluating of continuous pedestrian dynamic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    8. Kaji, Masaru & Inohara, Takehiro, 2017. "Cellular automaton simulation of unidirectional pedestrians flow in a corridor to reproduce the unique velocity profile of Hagen–Poiseuille flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 85-95.
    9. Simon Rahn & Marion Gödel & Rainer Fischer & Gerta Köster, 2021. "Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    10. Cristiani, E. & Menci, M. & Malagnino, A. & Amaro, G.G., 2023. "An all-densities pedestrian simulator based on a dynamic evaluation of the interpersonal distances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

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