Modeling the Pedestrian Flow Before Bottleneck Through Learning-Based Method

Nan Jiang, Lizhong Yang*, Richard Kwok Kit Yuen*, Chunjie Zhai

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a learning-based approach for modeling microscopic pedestrian movement behavior before bottleneck which contains two sub models – velocity direction model and velocity magnitude model. Datasets extracted from fifteen controlled experiments were trained through CART algorithm in this approach. Fifteen original scenarios are adopted to evaluate the performance of the proposed approach from both macroscopic and microscopic aspects. Simulation results show that the trajectories and density, velocity, flow vs. time curves and fundamental diagrams are basically consistent with those from corresponding experiments. Four evaluation metrics, final displacement error (FDE), duration error (DE), mean trajectory error (MTE) and relative distance error (RDE) are applied to quantitatively verify the precision of simulation. The mean of FDE, DE, and MTE are calculated to be 0.144 m, 0.112 s and 0.095 m respectively. With time intervals of 0.5 s, 1 s and 2 s, the mean RDE of all scenarios are respectively 0.7089, 0.4073 and 0.2515. Data from additional experiments of bottleneck scenarios and publicly available data of corridor scenarios are also introduced to prove the potential of general application of this approach. Comparisons and analysis indicate that the proposed approach has a good capability and reliability in simulating the pedestrian motion before the bottleneck. The models presented will be useful for passenger flow management and architectural design, also beneficial to understand the pedestrian movement mechanism in collective motion. © 2023 IEEE.
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number7
Online published20 Mar 2023
DOIs
Publication statusPublished - Jul 2023

Funding

This work was supported in part by the Research Grant Council of the Hong Kong Special Administrative Region, China, under Grant CityU T32-101/15-R and Grant CityU 11214221; in part by the Opening Fund of the State Key Laboratory of Fire Science (SKLFS) under Grant HZ2021-KF06; and in part by the National Natural Science Foundation of China under Grant 52106162

Research Keywords

  • Behavioral sciences
  • bottleneck
  • Computational modeling
  • Force
  • machine learning
  • Microscopy
  • Neural networks
  • Pedestrian flow
  • Predictive models
  • Trajectory
  • walking behavior

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