An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network

  • Jie XuEmail author
  • Yong Zhang
  • Yongzheng Jia
  • Chunxiao Xing
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)


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.


Road network Traffic prediction Residual CNN LSTM 



This research was financially supported by NSFC (91646202), the National High-tech R&D Program of China (SS2015AA020102), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program, Tsinghua University Initiative Scientific Research Program.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Jie Xu
    • 1
    • 2
    Email author
  • Yong Zhang
    • 1
    • 2
  • Yongzheng Jia
    • 3
  • Chunxiao Xing
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Research Institute of Information TechnologyBeijing National Research Center for Information Science and TechnologyBeijingChina
  3. 3.Institute of Interdisciplinary Information SciencesTsinghua UniversityBeijingChina

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