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Probabilistic speed–density relationship for pedestrian traffic

Author

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  • Nikolić, Marija
  • Bierlaire, Michel
  • Farooq, Bilal
  • de Lapparent, Matthieu

Abstract

We propose a probabilistic modeling approach to represent the speed–density relationship of pedestrian traffic. The approach is data-driven, and it is motivated by the presence of high scatter in the raw data that we have analyzed. We show the validity of the proposed approach, and its superiority compared to deterministic approaches from the literature using a dataset collected from a real scene and another from a controlled experiment.

Suggested Citation

  • Nikolić, Marija & Bierlaire, Michel & Farooq, Bilal & de Lapparent, Matthieu, 2016. "Probabilistic speed–density relationship for pedestrian traffic," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 58-81.
    • Handle: RePEc:eee:transb:v:89:y:2016:i:c:p:58-81
    DOI: 10.1016/j.trb.2016.04.002
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    References listed on IDEAS

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    Cited by:

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    2. Bosina, Ernst & Weidmann, Ulrich, 2017. "Estimating pedestrian speed using aggregated literature data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 1-29.
    3. Hänseler, Flurin S. & van den Heuvel, Jeroen P.A. & Cats, Oded & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "A passenger-pedestrian model to assess platform and train usage from automated data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 948-968.
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    5. Ji, Jingwei & Lu, Ligang & Jin, Zihao & Wei, Shoupeng & Ni, Lu, 2018. "A cellular automata model for high-density crowd evacuation using triangle grids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1034-1045.
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    7. Huang, Shenshi & Zhang, Teng & Lo, Siuming & Lu, Shouxiang & Li, Changhai, 2018. "Experimental study of individual and single-file pedestrian movement in narrow seat aisle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1023-1033.

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