Determining Optimal Critical Junctions for Real- time Traffic Monitoring for Transport GIS

  • Yang Yue
  • Anthony G. O. Yeh


Traffic data is the most important component of any transport GIS. They are mainly collected by traffic sensors (detectors). However, the installation of sensors is quite expensive. There have been studies on the finding of optimum location of inductive loop detector, the most widely used sensor in the past decades. However, with the advancement of new sensor technologies in recent years, many new sensors are now available for multi-lane and multi-direction traffic monitoring. Most of them are nonintrusive sensors which are more suitable be located at road junctions instead of roadways. Thus, different from previous studies on link-based sensor location, this paper explores method in determining critical road network junctions for the optimum location of nonintrusive sensors for monitoring and collecting real-time traffic data. The objective is to select the least number of junctions while maximally cover the road network. Since the problem is NP-complete, a greedy-based heuristic method is proposed and a numerical experiment is conducted to illustrate its efficiency.


Road Network Traffic Flow Vertex Cover Critical Junction Traffic Monitoring 
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  1. Bieneke L W (1978). Selected Topics in Graph Theory. Academic press, LondonGoogle Scholar
  2. Bianco L, Confessore G., and Reverberi P. (2001). A network based model for traffic sensor location with implications on O/D matrix estimates. Transportation Science 35(1): 50–60CrossRefGoogle Scholar
  3. Chrobok R, Wahel J., and Scherkenberg M. (2001). Traffic Forecast using simulations of large scale networks. 2001 IEEE Intelligent Transportaion Systems Conference Proceedings, Oakland, CA.Google Scholar
  4. Crescenz P, Kann V. (2003). A compendium of NP optimization problem.Google Scholar
  5. Diestel R (2000). Graph Theory (2nd Edition). Springer, New YorkGoogle Scholar
  6. Dolan A (1993). Networks and algorithms: an introductory approach. J. Wiley & Sons, ChichesterGoogle Scholar
  7. Homburger W S (1992). Fundamentals of Traffic Engineering. Institute of Transportation Studies, University of California at Berkeley„ BerkeleyGoogle Scholar
  8. Klein L A (2001). Sensor Technologies and Data Requirements for ITS. Artech House, BostonGoogle Scholar
  9. Lam W H K, Lo H. P. (1990). Accuracy of O-D estimates from traffic counts. Traffic Engineering and Control June: 358–367Google Scholar
  10. Mitzenmacher M (2003). Computer Science 124 — Data Structure and Algorithms.Google Scholar
  11. Perrin H J (1999). A Priority Mehod for Optimizing Network-Wide Traffic Detector Location. Salt Lake, Univeristy of Utah.Google Scholar
  12. Perrin H J (2002). Real time flow estimation using TMERT. Traffic Engineering and Control March: 90–91Google Scholar
  13. Skiena S S (1997). The Algorithm Design Manal. Springer, New YorkGoogle Scholar
  14. Weisstein E W (1999). Vertex Cover — from MathWorld, Scholar
  15. Yang B, Miller-Hooks E. (2002). Determining critical arcs for collecting real-time travel information. Transportation Research Record 1783: 34–41Google Scholar
  16. Yang H, Zhou J (1998). Optimal traffic counting locations for origin-destinaion matric estimation. Transportation Research B 32(2): 109–126CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yang Yue
    • 1
  • Anthony G. O. Yeh
    • 1
  1. 1.Centre of Urban Planning and Environmental ManagementThe University of Hong KongHong Kong SARP. R. China

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