Traffic time series analysis is important because of its use in traffic control and travel time prediction. In this paper, we discuss how to cluster traffic time series that have similar fluctuation patterns. We use simple average detrending method and only study the residual time series. Second, we use principle component analysis (PCA) on raw data and use the weight of the first d-components as the features of the time series. Third, we use k-means algorithm to cluster the traffic time series. Finally, we study the results of the clustering algorithm and discuss the origins of the clusters. In summary, the most important factors of clustering results are urban/rural area, direction and in/not in ramp entrance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fluctuation Similarity Modeling for Traffic Flow Time Series: A Clustering Approach


    Contributors:
    Jiang, Shan (author) / Wang, Shuofeng (author) / Li, Zhiheng (author) / Guo, Weiwei (author) / Pei, Xin (author)


    Publication date :

    2015-09-01


    Size :

    762994 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Fluctuation in Expressway Traffic Flow

    Tadaki, S. / Kikuchi, M. / Nakayama, A. et al. | British Library Conference Proceedings | 2005


    Time-Series Modeling for Forecasting Vehicular Traffic Flow in Dublin

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    New Modeling Approach for Predicting Disaggregated Time-Series Traffic Crashes

    Cai, Bowen / Quddus, Mohammed / Wang, Xuesong et al. | Transportation Research Record | 2023


    Traffic flow time series decomposition method

    WANG WEI / ZHOU WEI / WANG YUJIE et al. | European Patent Office | 2020

    Free access

    Traffic Flow Volume Fluctuation Analysis using MF-DFA

    Wang, Y. / Hu, J. / Li, L. et al. | British Library Conference Proceedings | 2009