Understanding users’ travel behaviors is an important subject in traffic science, which helps traffic management departments to formulate appropriate traffic control strategies. Travel mode identification is a critical aspect of analyzing users’ travel behaviors, aiming to precisely determine the travel modes of users and promote the sustainable development of urban intelligent transportation systems. In this article, we focus on identification methods for travel modes and propose a multi-scale convolutional neural network model based on channel attention mechanism. Our model efficiently extracts multi-scale sub-features and prioritizes key features. Additionally, to deeply extract effective information from the GPS data, we design 24 time-domain features and frequency-domain features and simplify them using principal component analysis and random forest algorithm. To verify the effectiveness of our method, we design five traditional machine learning models and two shallow deep learning models as comparative models, which are trained and tested on the Geolife dataset. Experimental results show that our approach is superior to the baseline models, achieving a maximum accuracy of 83.09%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Travel Mode Identification Based on Trajectory Data


    Additional title:

    Lecture Notes in Civil Engineering


    Contributors:
    Meng, Lingyun (editor) / Qian, Yongsheng (editor) / Bai, Yun (editor) / Lv, Bin (editor) / Tang, Yuanjie (editor) / Zhang, Ruonan (author) / Li, Dewei (author) / Huang, Yue (author)

    Conference:

    International Conference on Traffic and Transportation Studies ; 2024 ; Lanzhou, China August 23, 2024 - August 25, 2024



    Publication date :

    2024-11-21


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Addressing robust travel mode identification with individual trip‐chain trajectory noise reduction

    Jiaqi Zeng / Guozheng Zhang / Youwei Hu et al. | DOAJ | 2023

    Free access

    Addressing robust travel mode identification with individual trip‐chain trajectory noise reduction

    Zeng, Jiaqi / Zhang, Guozheng / Hu, Youwei et al. | Wiley | 2023

    Free access


    TRAVEL TRAJECTORY GENERATION METHOD AND TRAVEL TRAJECTORY GENERATION DEVICE

    HIRAMATSU MACHIKO / YAMAMURA TOMOHIRO | European Patent Office | 2021

    Free access

    TRAVEL TRAJECTORY GENERATION DEVICE AND TRAVEL TRAJECTORY GENERATION METHOD

    INOU HIROSHI / MATSUMOTO TOSHIKI / MIYAKE TEPPEI et al. | European Patent Office | 2017

    Free access