Customized bus (CB) is a kind of demand-responsive and one-stop transit service for commuters with similar travel demands. In actual applications, the bus companies identify commuter demands including origin-destination (OD) flow and departure time using online surveys, and manually plan the CB routes, which is inefficient and costly. This study aims to identify spatial patterns of travel demands using a clustering algorithm based on vehicle trajectory data, and automatically extract the potential CB route from each cluster. The clustering algorithms are based on similarity measurements. However, the existing measurements cannot be used to synchronously identify the multiple characteristics of vehicle trajectories, such as OD location, direction, and multiple sub-sequences. To address this issue, we propose a comprehensive similarity (CS) to simultaneously evaluate all the characteristics. Subsequently, we utilize the density-based spatial clustering of applications with noise (DBSCAN) to divide the trajectories into several groups, and determine the trade-off parameters in the DBSCAN using vlseKriterijumska optimizacija i kompromisno resenje (VIKOR), which means a multi-criteria optimization and compromise solution. Then, a case study is conducted based on one-week taxi trajectory data collected in Suzhou, China. The extracted results are compared to the existing routes designed by bus companies, providing a coverage rate of 66.7%. Finally, the advantages of the proposed method were discussed from the two perspectives of clustering algorithm and trajectory similarity measurement. The proposed method can help traffic management authorities and bus companies to plan and design new CB routes.


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    Title :

    Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory Clustering


    Contributors:
    Ma, Dongfang (author) / Fang, Bo (author) / Ma, Weihao (author) / Wu, Xiaodong (author) / Jin, Sheng (author)

    Published in:

    Publication date :

    2023-11-01


    Size :

    3828614 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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