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To establish a model for estimating passenger walking time within high-speed railway stations and enhance station operational efficiency, this study utilizes WIFI probe technology to analyze and estimate the characteristics and walking time of passengers in different areas at railway stations. First, the process of passenger flow collection using WIFI probes is summarized, and passenger walking information is collected under various temporal and spatial scenarios. Subsequently, based on the spatial layout and passenger density of different areas within the station, walking time calculation models are proposed respectively using the Bureau of Public Roads (BPR) function and logarithmic function models. Finally, the Beijing south railway station is taken as a case study for model validation. The results indicate that the WIFI probes exhibit excellent detection performance, with the BPR and logarithmic function models fitted based on probe data achieving accuracies of 92.63% and 93.28%, respectively, under different passenger densities. This validates the applicability and prediction accuracy of the proposed models.

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Estimation of Passenger Walking Time at the Railway Station Based on WIFI Probe Data

  • 会议论文

  • 发表日期：

    2025年

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  • Feng Liu,
  • Guohua Song,
  • Xumei Chen,
  • Xiaoxi Liu,
  • Xiaoyu He,
  • Ming Wang,
  • Shu Xi,
  • Yue Du

摘要

To establish a model for estimating passenger walking time within high-speed railway stations and enhance station operational efficiency, this study utilizes WIFI probe technology to analyze and estimate the characteristics and walking time of passengers in different areas at railway stations. First, the process of passenger flow collection using WIFI probes is summarized, and passenger walking information is collected under various temporal and spatial scenarios. Subsequently, based on the spatial layout and passenger density of different areas within the station, walking time calculation models are proposed respectively using the Bureau of Public Roads (BPR) function and logarithmic function models. Finally, the Beijing south railway station is taken as a case study for model validation. The results indicate that the WIFI probes exhibit excellent detection performance, with the BPR and logarithmic function models fitted based on probe data achieving accuracies of 92.63% and 93.28%, respectively, under different passenger densities. This validates the applicability and prediction accuracy of the proposed models.