Real-Time Traffic-Speed Learning and Prediction for Dynamic Route Planning

Dynamic route planning is a promising way to improve the quality of route planners in navigation systems based on real-time traffic data. Real-time traffic data offer information for predicting fluctuations and changes in traffic speeds on links of a road network which routing algorithms can take into account. The goal of this study is to introduce and test a system of incremental learning of link speed profiles based on real-time traffic data. In the proposed system, day-by-day fluctuation in traffic conditions on a link is represented by a discrete distribution of speed profiles. Learning the distribution is formulated as a dynamic version of the K-means clustering problem. Based on a dynamic K-means algorithm the system learns the discrete distribution (K means) incrementally and continuously. Furthermore, in the short term, the system continuously updates its current belief of the actual realization of the link’s speed profile based on continuous speed measurements from a sensor. The system is tested based on numerical experiments where sensor data are simulated. The results indicate that, in the long-term, the system effectively learns assumed distributions of speed profiles and, in the short term, adequately responds to real-time traffic information to improve short-term traffic speed prediction. Therefore, it is concluded that the system offers a promising way to implement dynamic routing in current route planners for navigation systems.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADB30(6) Paper Review Group #2.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Arentze, Theo A
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 17p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Algorithms; Automobile navigation systems; Real time information; Routing; Traffic data; Traffic forecasting; Traffic speed
• Uncontrolled Terms: Route planning
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01516077
• Record Type: Publication
• Report/Paper Numbers: 14-1226
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2014 9:05AM