In order to solve the problem of dividing the traffic pattern of the OD extracted by the mobile phone signaling, using resident survey data and network crawling data as samples, features such as travel attributes and traveler attributes are extracted and equivalently processed to construct model input data equivalent to mobile phone signaling data. Selecting random forest, support vector machine, and neural network machine learning model helps divide the three modes of transportation: car travel, public transportation, and other travel modes. Through multi-index evaluation and case verification, the results show that the random forest model is a better overall model with a prediction accuracy of 70%. The prediction results are stable and reliable and have certain practical application value.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Pattern Division Based on Mobile Signaling Data


    Contributors:
    Yu, Luyi (author) / Zhu, Xun (author) / Zhou, Shasha (author) / Wang, Jingyuan (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 3929-3936


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic travel model construction method based on mobile phone signaling data

    CUI YI / GAO TIAN / CHANG JIANYONG et al. | European Patent Office | 2021

    Free access

    Multimode traffic distribution model construction method based on mobile phone signaling data

    SHEN JIAN / LU ZHENBO / XUE GUANGMING et al. | European Patent Office | 2020

    Free access

    Estimating urban road traffic states using mobile network signaling data

    Derrmann, Thierry / Frank, Raphael / Viti, Francesco et al. | IEEE | 2017


    Comprehensive traffic distribution method based on mobile phone signaling

    YANG WEIDONG / ZHU ZHIBANG / LI DAWEI et al. | European Patent Office | 2023

    Free access