This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles’ mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks


    Contributors:
    Ip, Andre (author) / Irio, Luis (author) / Oliveira, Rodolfo (author)


    Publication date :

    2021-04-01


    Size :

    3317592 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

    Messaoud, Kaouther / Yahiaoui, Itheri / Verroust-Blondet, Anne et al. | IEEE | 2019


    Vehicle trajectory prediction based on LSTM network

    Yang, Zhifang / Liu, Dun / Ma, Li | IEEE | 2022


    Transform and LSTM-based vehicle trajectory prediction method

    CHENG DENGYANG / GU XIANG / QIAN CONG et al. | European Patent Office | 2023

    Free access

    Aircraft Trajectory Prediction Based on Residual Recurrent Neural Networks

    Fan, Zhonghang / Lu, Junqi / Qin, Zhanghao | IEEE | 2023