MarkovmodelLaneChange.pdf (282.28 kB)
Estimation of traffic densities for multilane roadways using a Markov model approach
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
© IEEEVersion
- AM (Accepted Manuscript)
Publication date
2012Notes
© 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
0278-0046Publisher version
Language
- en