Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network

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  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_27,
        author={Jie Xu and Yong Zhang and Yongzheng Jia and Chunxiao Xing},
        title={An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={Road network Traffic prediction Residual CNN LSTM},
        doi={10.1007/978-3-030-12981-1_27}
    }
    
  • Jie Xu
    Yong Zhang
    Yongzheng Jia
    Chunxiao Xing
    Year: 2019
    An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_27
Jie Xu,*, Yong Zhang,*, Yongzheng Jia1,*, Chunxiao Xing,*
  • 1: Tsinghua University
*Contact email: xuj15@mails.tsinghua.edu.cn, zhangyong05@tsinghua.edu.cn, jiayz13@mails.tsinghua.edu.cn, xingcx@tsinghua.edu.cn

Abstract

Recently years, traffic prediction has become an important and challenging problem in smart urban traffic computing, which can be used for government for road planning, detecting bottle-neck congestions roads, pollution emissions estimating and so on. However, former data mining algorithms mainly address the problem by using the traditional mathematical or statistical theories, and they were impossible to model the spatial and temporal relationship simultaneously. To address these issues, we propose an end-to-end neural network named C-LSTM to predict the traffic congestion at next time interval. More specifically, the C-LSTM is based on CNN and LSTM to collectively capture the spatial-temporal dependencies on the road network. Inspired by the procedure of handling the image by CNN, the city-wide traffic maps are first converted into a series of static images like the video frame and then are fed into a deep learning architecture, in which CNN extracts the spatial characteristics, and LSTM extracts the temporal characteristics. In addition, we also consider some external factors to further improve the prediction accuracy. Extensive experiments on reality Beijing transportation datasets demonstrate the superiority of our method.