Promoting public transit is a well-recognized policy for sustainable urban transport development. Transit demand analysis proves to be a challenging task in fast growing cities, partially due to the lack of reliable data and applicable techniques for rapidly changing urban contexts. This paper presents an effort to meet the challenge by developing a framework to estimate peak-hour boarding at light-rail transit (LRT) stations. The core part of the framework is an accessibility-weighted ridership model that multiplies potential demand by integral LRT accessibility. Potential demand around LRT stations is generated by using a distance-decay function. The integral LRT accessibility is a route-level factor that indicates the degree of attractiveness to LRT travel for stations in an LRT corridor. A case study in Wuhan, China, shows that the proposed method produces results useful for improving transit demand analysis.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Estimating light-rail transit peak-hour boarding based on accessibility at station and route levels in Wuhan, China




    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.80 / 74.75 / 55.80 Verkehrswesen, Transportwesen: Allgemeines / 74.75 Verkehrsplanung, Verkehrspolitik
    Local classification TIB:    770/7000



    Estimating light-rail transit peak-hour boarding based on accessibility at station and route levels in Wuhan, China

    Huang, Zhengdong / Zhang, Ming / Liu, Xuejun | Taylor & Francis Verlag | 2017


    Subway boarding device for rail transit

    LI BOHAO | European Patent Office | 2021

    Free access

    Urban rail transit station platform and boarding and alighting method

    WANG ZHE | European Patent Office | 2023

    Free access

    Managing rail transit peak-hour congestion with step fare schemes

    Lu, Xiao-Shan / Zhang, Xin / Long, Jiancheng et al. | Taylor & Francis Verlag | 2020