Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Traffic Density Estimation for Multiple Segment Freeways

Authors: Karandeep Singh, Baibing Li

Abstract:

Traffic density, an indicator of traffic conditions, is one of the most critical characteristics to Intelligent Transport Systems (ITS). This paper investigates recursive traffic density estimation using the information provided from inductive loop detectors. On the basis of the phenomenological relationship between speed and density, the existing studies incorporate a state space model and update the density estimate using vehicular speed observations via the extended Kalman filter, where an approximation is made because of the linearization of the nonlinear observation equation. In practice, this may lead to substantial estimation errors. This paper incorporates a suitable transformation to deal with the nonlinear observation equation so that the approximation is avoided when using Kalman filter to estimate the traffic density. A numerical study is conducted. It is shown that the developed method outperforms the existing methods for traffic density estimation.

Keywords: Density estimation, Kalman filter, speed-densityrelationship, Traffic surveillance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055437

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1448

References:


[1] T. Z. Qiu,., X. Y. Lu, A. H. F. Chow, & S. Shladover, "Real-time density estimation on freeway with loop detector and probe data," Transportation Science, 2009, vol. 31, pp. 324-335.
[2] D. Gazis, & C. Liu, "Kalman filtering estimation of traffic counts for two network links in tandem," Transportation Research Part B, 2003, vol. 37(8), pp. 737-745.
[3] Y. Wang, & M. Papageorgiou, "Real-time freeway traffic state estimation based on extended Kalman filter: A general approach," Transportation Research Part B, 2005, vol. 39(2), pp. 141-167.
[4] J. S. Drake, J. L. Schofer, and A. D. May. "A Statistical Analysis of Speed Density Hypotheses," Highway Research Record, 1967, vol. 154, pp. 53-87.
[5] B. Li, "A non-Gaussian Kalman filter with application to the estimation of vehicular speed," Technometrics, 2009, vol. 51(2), pp. 162-172.
[6] B. D. Greenshields, "The Photographic Method of studying Traffic Behaviour," Proceedings of the 13th Annual Meeting of the Highway Research Board, 1933
[7] B. D. Greenshields, "A study of highway capacity," Proceedings Highway Research Record, Washington, 1935, vol. 14, pp. 448-477.
[8] M. W. Szeto, , & D. C. Gazis, "Application of kalman filtering to the surveillance and control of traffic systems," Transportation Science, 1972, vol. 6(4), pp. 419.
[9] X. Sun, L. Mu├▒oz, & R. Horowitz, "Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application," Proceedings of the 2004 American Control Conference, pp. 2098-2103.
[10] G. Vigos, M. Papageorgiou, & Y. Wang, "Real-time estimation of vehicle-count within signalized links," Transportation Research Part C, 2008, vol. 16(1), pp. 18-35.