A Real-Time Urban Traffic Detection Algorithm Based on Spatio-Temporal OD Matrix in Vehicular Sensor Network

DOI: 10.4236/wsn.2010.29080   PDF   HTML     6,424 Downloads   10,999 Views   Citations


Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on original- destination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.

Share and Cite:

K. Zhang and G. Xue, "A Real-Time Urban Traffic Detection Algorithm Based on Spatio-Temporal OD Matrix in Vehicular Sensor Network," Wireless Sensor Network, Vol. 2 No. 9, 2010, pp. 668-674. doi: 10.4236/wsn.2010.29080.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. Bhattacharjee, K. C. Sinha and J. V. Krogmeier, “Modeling the Effects of Traveler Information on Freeway Origin-Destination Demand Prediction,” Transportation Research, Vol. 6, No. 9, 2001, pp. 381-398.
[2] G. L. Chang and J. F. Wu, “Recursive Estimation of Time-Varying Origin-Destination Flows from Traffic Counts in Freeway Corridors,” Transportation Research Part B, Vol. 28, No. 2, 1994, pp. 141-160.
[3] A. Medina, N. Taft, K. Salamatian, S. Bhattacharyya and C. Diet. “Traffic Matrix Estimation: Existing Techniques and New Directions,” Proceedings of ACM SIGCOMM, 2002.
[4] Y. Zhang, M. Roughan, W. Willinger and L. L. Qiu, “Spatio-Temporal Compressive Sensing and Internet Traffic Matrices,” Proceedings of ACM SIGCOMM, 2009.
[5] Y. Cho, “Estimating Velocity Fields on a Freeway from Low-Resolution Videos,” IEEE Transactions on Intelligent Transportation Systems, Vol.7, No. 4, 2007, pp. 463-469.
[6] W. Lin and C. Daganzo, “A Simple Detection Scheme for Delay-Inducing Freeway Incidents,” Transportation Research, Vol. 31A of Part A, 1997, pp. 141-155.
[7] B. Coifman, “Identifying the Onset of Congestion Rapidly with Existing Traffic Detector,” Transportation Research, Vol. 37 of Part A, 2003, pp. 277-291.
[8] J. Yoon, B. Noble and M. Liu, “Surface Street Traffic Estimation,” Proceedings of ACM Mobicom, 2007.
[9] H. Z. Zhu, Y.M. Zhu, M. L., Li and L. M. Ni, “SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data,” Proceedings of ACM INFOCOM, 2009.
[10] M. McNally, J. Marca, C. Rindty and A. Koos. “Tracer: In-Vehicle, Gps-Based, Wireless Technology for Traffic Surveillance and Management,” Technical Report UCB- ITS-PRR-2003-23, California Partners for Advanced Transit and Highways (PATH), July 2003.
[11] J. Ygnace, C. Drane, Y. Yim and R. Lacvivier. “Travel Time Estimation on the San Francisco Bay Area Network Using Cellular Phones as Probes,” Technical Report UCB-ITS-PWB-2000-18, California Partners for Advanced Transit and Highways (PATH), September 2000.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.