Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inherent traffic patterns and combine the STD at the prediction time interval to consider the future disturbances. The experimental results on two real-world datasets demonstrate that DeepSTD outperforms the state-of-the-art methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction


    Contributors:


    Publication date :

    2020-09-01


    Size :

    4736964 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic speed prediction using a deep neural network to accommodate citywide spatio-temporal correlations

    LEE YONG JIN / SOHN KEE MIN | European Patent Office | 2020

    Free access



    Method for traffic flow prediction based on spatio-temporal correlation mining

    SHI SHIXIONG / YANG SU | European Patent Office | 2017

    Free access

    METHOD FOR TRAFFIC FLOW PREDICTION BASED ON SPATIO-TEMPORAL CORRELATION MINING

    SHI SHIXIONG / YANG SU | European Patent Office | 2016

    Free access