Travel time prediction for float car system based on time series

  • Authors:

  • Tongyu Zhu;Xueping Kong;Weifeng Lv;Yuan Zhang;Bowen Du

  • Affiliations:

  • State Key Laboratory of Software Development Environment, Beihang University, Beijing, China;State Key Laboratory of Software Development Environment, Beihang University, Beijing, China;State Key Laboratory of Software Development Environment, Beihang University, Beijing, China;State Key Laboratory of Software Development Environment, Beihang University, Beijing, China;State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

  • Venue:
  • ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
  • Year:
  • 2010

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Abstract

Recently, the Float Car technology is playing a more and more important role in real-time traffic service systems because it can collect real-time traffic information with low cost, high coverage and high efficiency. Meanwhile, the ability to accurately predict travel times in transportation networks is becoming a critical component for many Intelligent Transportation Systems. This paper focuses on the research of travel time prediction method based on Float Car Data. To gain the inherent characteristic of traffic information, a mechanism of dynamically extracting traffic periodic trends through the statistical analysis of historical data is present. On the basis of it, a series of improvements based on time series are proposed to predict the travel time information. The Float Car Data in Beijing are used as experiment data to verify the methods.