Deep Architecture for Traffic Flow Prediction

  • Wenhao Huang
  • Haikun Hong
  • Man Li
  • Weisong Hu
  • Guojie Song
  • Kunqing Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8347)


Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail at providing favorable results duo to 1)shallow in architecture;2)hand engineered in features. In this paper, we propose a deep architecture consists of two parts: a Deep Belief Network in the bottom and a regression layer on the top. The Deep Belief Network employed here is for unsupervised feature learning. It could learn effective features for traffic flow prediction in an unsupervised fashion which has been examined effective for many areas such as image and audio classification. To the best of our knowledge, this is the first work of applying deep learning approach to transportation research. Experiments on two types of transportation datasets show good performance of our deep architecture. Abundant experiments show that our approach could achieve results over state-of-the-art with near 3% improvements. Good results demonstrate that deep learning is promising in transportation research.


Deep Learning Deep Belief Nets Traffic Flow Prediction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wenhao Huang
    • 1
  • Haikun Hong
    • 1
  • Man Li
    • 2
  • Weisong Hu
    • 2
  • Guojie Song
    • 1
  • Kunqing Xie
    • 1
  1. 1.Key Laboratory of Machine Perception, Ministry of EducationPeking UniversityBeijingChina
  2. 2.NEC LabsChina

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