A computation method on time-dependent accessibility of urban rail transit networks for the last service

    Yao Chen Affiliation
    ; Baohua Mao Affiliation
    ; Yun Bai Affiliation
    ; Zhujun Li Affiliation
    ; Jimeng Tang Affiliation


Urban rail transit networks seldom provide 24-hour service. The last train is the latest chance for passengers. If passengers arrive too late to catch the last train, the path becomes inaccessible. The network accessibility thus varies depending on the departure time of passenger trips. This paper focuses on the computation method on the time-dependent accessibility of urban rail transit networks in order to facilitate the itinerary planning of passengers. A label setting algorithm is first designed to calculate the latest possible times for Origin–Destination (O–D) pairs, which is the latest departure times of passengers from the origins such that the destinations can be reach successfully. A searching approach is then developed to find the shortest accessible path at any possible departure times. The method is applied in a real-world metro network. The results show that the method is a powerful tool in solving the service accessibility problem. It has the ability to allow passengers to plan an optimal itinerary. Comparison analysis indicates that the proposed method can provide exact solutions in much shorter time, compared with a path enumeration method. Extensive tests on a set of random networks indicate that the method is efficient enough in practical applications. The execution time for an O–D pair on a personal computer with 2.8 GHZ CPU and 4GB of RAM is only 1.2 s for urban rail transit networks with 100 transfer stations.

Keyword : urban rail transit network, accessibility, itinerary planning, last train, timetable, label setting algorithm

How to Cite
Chen, Y., Mao, B., Bai, Y., Li, Z., & Tang, J. (2020). A computation method on time-dependent accessibility of urban rail transit networks for the last service. Transport, 35(1), 26-36.
Published in Issue
Feb 26, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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