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Passenger arrival distribution model and riding guidance on an urban rail transit platform

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  • Ding, Heng
  • Di, Yunran
  • Zheng, Xiaoyan
  • Liu, Kai
  • Zhang, Weihua
  • Zheng, Lingling

Abstract

Existing methods to improve the operational efficiency and service level of urban rail transit (URT) systems focus on forecasting passenger flow in macro networks and optimizing metro vehicle operation strategies. Among the factors affecting passenger comfort in vehicles, the passenger arrival distribution is the most direct but is not sufficiently taken into account. This study provides a riding guidance method for passengers on the URT platform to improve the uniformity of the passenger distribution among carriages. First, a passenger arrival distribution model is proposed on the basis of the influences of the queue length at the door, the number of people in the carriage and the travel distance on the passengers’ choice of carriage. Second, taking the maximum spatial comfort degree of every passenger as an objective, a passenger riding guidance model on the URT platform is proposed. Hefei Metro Line 2 is taken as an example for the numerical case study, and the results show that the guidance method can balance the passenger flow distribution and improve the spatial comfort level of passengers when the passenger flow is approximately 65 to 135 persons per 5 min.

Suggested Citation

  • Ding, Heng & Di, Yunran & Zheng, Xiaoyan & Liu, Kai & Zhang, Weihua & Zheng, Lingling, 2021. "Passenger arrival distribution model and riding guidance on an urban rail transit platform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    • Handle: RePEc:eee:phsmap:v:571:y:2021:i:c:s0378437121001199
    DOI: 10.1016/j.physa.2021.125847
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    References listed on IDEAS

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    Cited by:

    1. Zheng, Zhongxing & Wang, Heng & Liu, Weiming & Peng, Liru, 2023. "Toward real-time congestion measurement of passenger flow on platform screen doors based on surveillance videos analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    2. Yu, Liping & Liu, Huiran & Fang, Zhiming & Ye, Rui & Huang, Zhongyi & You, Yayun, 2023. "A new approach on passenger flow assignment with multi-connected agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. 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).
    4. Huang, Kang & Wu, Jianjun & Sun, Huijun & Yang, Xin & Gao, Ziyou & Feng, Xujie, 2022. "Timetable synchronization optimization in a subway–bus network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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