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Traffic Flow Prediction with Improved SOPIO-SVR Algorithm

  • Xuejun Cheng
  • Lei RenEmail author
  • Jin Cui
  • Zhiqiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10228)

Abstract

In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.

Keywords

Traffic flow prediction SOPIO-MSVR Classification model 

Notes

Acknowledgments

The research is supported by the NSFC (National Science Foundation of China) Projects (No. 61572057) in China, the National High-Tech Research and Development Plan of China under Grant No. 2015AA042101.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xuejun Cheng
    • 1
    • 2
  • Lei Ren
    • 1
    • 2
    Email author
  • Jin Cui
    • 1
    • 2
  • Zhiqiang Zhang
    • 1
    • 2
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Engineering Research Center of Complex Product Advanced Manufacturing SystemMinistry of EducationBeijingChina

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