Travel time, an important traffic flow parameter, has always been a research focus and hot spot in the field of transportation. In order to predict the traffic state and evaluate the traffic efficiency of the link, the travel time is classified based on the electronic police data. First, the travel time is defined based on the electronic police work mechanism, and the relevant data is extracted from the database to make a travel time matching table of the link. We analyzed the characteristics of travel time including cycle and aggregation. Finally, 274 cuts, the traffic volume of which were more than 10, were obtained using the headway time to cut data from a certain link of Suzhou Industrial Park with the help of characteristic of cycle. The DBSCAN algorithm is used to cluster all the slices, and three categories are obtained. The travel time distribution parameters of the link are proposed based on the travel time characteristics. The relationship between the three types of cuts, the travel time distribution parameters of the link and the traffic flow parameters is analyzed and traffic state is predicted.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Classification of Travel Time for Electronic Police Based on the DBSCAN Algorithm


    Contributors:
    Wang, Hao (author) / Tian, Heng (author) / Zhang, Yibo (author) / Gu, Bao (author)


    Publication date :

    2021-04-01


    Size :

    2588891 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

    Xu, Xiaofeng / Cui, Deqaing / Li, Yun et al. | Online Contents | 2021


    Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

    Xu Xiaofeng / Cui Deqaing / Li Yun et al. | DOAJ | 2021

    Free access

    Collision Risk Hotspot Identification Based on AIS Data and DBSCAN Algorithm

    Sun, Jiawei / Zhang, Jinfen / Tian, Wuliu et al. | IEEE | 2023


    Iterative DBSCAN (I-DBSCAN) to Identify Aggressive Driving Behaviors within Unlabeled Real-World Driving Data

    Marks, Charles / Jahangiri, Arash / Machiani, Sahar Ghanipoor | IEEE | 2019