Lane changing is a fundamental driving task and is closely related to traffic operation. The safety performance of vehicle driving and traffic flow is supposed to be substantially improved if lane-changing behavior can be precisely predicted. To this end, a model based on the Gated Recurrent Unit (GRU) is proposed in this study for freeway on-ramp lane-changing behavior forecasting. One specific feature of the model is that it enables the filtering out of the lateral oscillation behavior and helps enhance forecast accuracy. The experiment results show that the model achieves an accuracy of 96.85% for lane-changing behavior forecasting, and outperforms the GRU model without lateral acceleration input and the LSTM model by 5.12% and 4.51%, respectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Forecasting Freeway On-Ramp Lane-Changing Behavior Based on GRU


    Contributors:
    Cui, Jieming (author) / Yu, Guizhen (author) / Zhou, Bin (author) / Liu, Qiujun (author) / Guan, Zhengguo (author)


    Publication date :

    2021-10-06




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Modeling Freeway Lane Changing Behavior

    Koutsopoulos, H. N. / Ben-Akiva, M. E. / Ahmed, K. I. et al. | British Library Conference Proceedings | 1996


    Intensity of Lane Changing at a Freeway Ramp Weave Section

    Bham, G. H. / American Society of Civil Engineers | British Library Conference Proceedings | 2005



    Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

    Lanfang Zhang / Cheng Chen / Jiayan Zhang et al. | DOAJ | 2018

    Free access