The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.


    Access

    Download


    Export, share and cite



    Title :

    Spatiotemporal Traffic Flow Prediction with KNN and LSTM


    Contributors:
    Xianglong Luo (author) / Danyang Li (author) / Yu Yang (author) / Shengrui Zhang (author)


    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    SPATIOTEMPORAL TRAFFIC FLOW PREDICTION SYSTEM

    KIM EUN YI | European Patent Office | 2016

    Free access

    Intersection traffic flow prediction method based on LSTM

    ZHANG HUI / LI ZHAOCHUAN / WANG GUANJUN et al. | European Patent Office | 2024

    Free access

    Traffic flow prediction method based on LSTM-Attention

    QIN XIAOLIN / LIU JIACHEN / SONG LIXIANG et al. | European Patent Office | 2021

    Free access

    Instant traffic flow prediction method based on Conv-LSTM

    WU LIJUN | European Patent Office | 2023

    Free access

    Short-term traffic flow prediction with LSTM recurrent neural network

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