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Estimation of traffic densities for multilane roadways using a Markov model approach

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journal contribution
posted on 25.04.2013 by Karandeep Singh, Baibing Li
Inductive loop detectors are widely deployed in strategic roadway networks. This paper investigates recursive estimation of traffic densities using the information provided by loop detectors. The existing studies for multi-lane roadways mainly focus on the scenario where vehicles’ lane change movements are not common and can be ignored. This research, however, takes into consideration of lane change effect in traffic modeling and incorporates a Markov chain into the state space model to describe the lane-change behavior. We update the traffic density estimate using the Kalman filter. To avoid the approximation due to the linearization of the nonlinear observation equation in the extended Kalman filter, we have considered a suitable transformation. Numerical studies were carried out to investigate the performance of the developed approach. It is shown that it outperforms the existing methods.

History

School

  • Business and Economics

Department

  • Business

Citation

SINGH, K. and LI, B., 2012. Estimation of traffic densities for multilane roadways using a Markov model approach. IEEE Transactions on Industrial Electronics, 59 (11), pp.4369-4376.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2012

Notes

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

0278-0046

Language

en

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