As urbanization intensifies, the status of the traffic situation predict is becoming more and more prominent. The urban traffic flow is influenced by many factors and is characterized by strong randomness. This paper combines MSE and Adam to construct a linear LSTM to realize the prediction of short-term traffic flow based on time series. The experiment result shows that LSTM can gain the periodic features of the traffic flow. It has small error and high precision for the short-term prediction of the traffic flow based on time series, which verifies the validity of LSTM.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Application of LSTM in Short-term Traffic Flow Prediction


    Contributors:


    Publication date :

    2020-09-01


    Size :

    455977 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Short-Term Traffic Flow Prediction: Using LSTM

    Poonia, Pregya / Jain, V. K. | IEEE | 2020


    Expressway Short-Term Traffic Flow Prediction Based on CNN-LSTM

    Ye, Ting / Zou, Fumin / Guo, Feng | Springer Verlag | 2024



    Short-term traffic flow prediction with LSTM recurrent neural network

    Kang, Danqing / Lv, Yisheng / Chen, Yuan-yuan | IEEE | 2017


    Short-term traffic flow prediction method based on improved LSTM

    GAN YONGHUA / JIANG XUEFENG / HU JINGSONG et al. | European Patent Office | 2021

    Free access