Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward.


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    Title :

    Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain


    Contributors:
    Wenbin Yao (author) / Caijun Chen (author) / Hongyang Su (author) / Nuo Chen (author) / Sheng Jin (author) / Congcong Bai (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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