Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network




    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.21 Kraftfahrzeuge / 55.21
    Local classification TIB:    275/7020




    Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach

    Zheng, W. / Lee, D.-H. / Shi, Q. | British Library Online Contents | 2006


    Short-Term Traffic Flow Prediction Based on Bayesian Fusion

    Zhang, Yu-Ning / Wang, Jing-Sheng / Lu, Hao et al. | TIBKAT | 2020


    Short-Term Traffic Flow Prediction Based on Bayesian Fusion

    Zhang, Yu-Ning / Wang, Jing-Sheng / Lu, Hao et al. | ASCE | 2020