The development of information technology gives rise to explosive growth of the amount of data. As a result, a more effective data mining method in pattern recognition is called into existence, which can properly reflect the inherent daily activity structure of metro travelers. This study is aimed to enrich the traditional clustering methods and provide practical information in dealing with traffic volume variation to the metro system operations. In this study, daily metro origin-destination (OD) data come from smart card records of Shenzhen, China, which cover 290 days and 118 stations. Principal component analysis (PCA) and singular value decomposition (SVD) are applied to conduct dimensionality reduction. Affinity propagation is then chosen to cluster the dimensionality reduced matrix to identify demand patterns of the metro OD matrix. Eleven representative categories are clustered and shown.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Clustering Daily Metro Origin-Destination Matrix in Shenzhen China



    Published in:

    Publication date :

    2015-03-23


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    The Origin–Destination Matrix Development

    Dragu Vasile / Roman Eugenia Alina | DOAJ | 2019

    Free access

    First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

    Hetian Chai / Xiaopeng Tian / Huimin Niu | DOAJ | 2022

    Free access


    Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

    Xu, Yuhang / Lyu, Yan / Xiong, Guangwei et al. | IEEE | 2023

    Free access