Accurately predicting the bus travel time is important for improving bus service levels and enhancing the attractiveness of the bus service. This study first integrated multi-source data to construct multiple influencing factors, including bus arrival time data, spatial attributes of bus stops and routes, smart card data, and weather data. Random forest was then used to quantitatively analyze the impact of various influencing factors on bus travel time. The convolutional neural network-long short-term memory-attention mechanism (CNN-LSTM-ATTENTION) model was proposed to predict the bus travel time. One month’s multi-source data from Beijing was used to build the model. The results indicate that the travel time of the previous bus has the highest importance, therefore it is necessary to incorporate this feature for predicting bus arrival time to enhance prediction accuracy. The CNN-LSTM-ATTENTION model can reduce the mean square error by up to 29.7% compared to the traditional LightGBM model. Considering the travel time of the previous bus reduces the mean square error by 12.8% compared to without considering it.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Bus Travel Time Prediction Based on Multiple Influencing Factors and CNN-LSTM-ATTENTION Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Wang, Yanhui (editor) / Easa, Said (editor) / Lin, Pengfei (author) / Chen, Yuzhuo (author) / Ouyang, Yinuo (author) / Yang, Nuo (author) / Weng, Jiancheng (author)

    Conference:

    International Conference on SmartRail, Traffic and Transportation Engineering ; 2024 ; Chongqing, China October 25, 2024 - October 27, 2024



    Publication date :

    2025-07-19


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Travel time prediction with LSTM neural network

    Yanjie Duan / Yisheng Lv / Fei-Yue Wang | IEEE | 2016


    Traffic Density Based Travel-Time Prediction With GCN-LSTM

    Katayama, Hiroki / Yasuda, Shohei / Fuse, Takashi | IEEE | 2022


    Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

    Hao Li / Zijian Wang / Xiantong Li et al. | DOAJ | 2023

    Free access

    Travel Time Fusion Based on Influencing Factors Classification

    Yang, Zhaosheng / Gao, Xueying | ASCE | 2011


    Bus travel time prediction method based on space-time diagram attention network

    LIN PENGFEI / ZHAO SHICHANG / OUYANG YINUO et al. | European Patent Office | 2025

    Free access