Vehicle density estimation of freeway traffic with unknown boundary demand-supply : an IMM approach

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Zhang, Liguo and Mao, Xuerong (2015) Vehicle density estimation of freeway traffic with unknown boundary demand-supply : an IMM approach. IET Control Theory and Applications, 9 (13). pp. 1989-1995. ISSN 1751-8644 (https://doi.org/10.1049/iet-cta.2014.1251)

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Abstract

As distributed parameter systems, dynamics of freeway traffic are dominated by the current traffic parameter and boundary fluxes from upstream/downstream sections or on/off ramps. The difference between traffic demand-supply and boundary fluxes actually reflects the congestion level of freeway travel. This paper investigates simultaneous traffic density and boundary flux estimation with data extracted from on-road detectors. The existing studies for traffic estimation mainly focus on the traffic parameters (density, velocity, etc.) of mainline traffic and ignore flux fluctuations at boundary sections of the freeway. We propose a stochastic hybrid traffic flow model by extending the cell transmission model (CTM) with Markovian multi-mode switching. A novel interacting multiple model (IMM) filtering for simultaneous input and state estimation is developed for discrete-time Markovian switching systems with unknown input. A freeway segment of Interstate 80 East (I-80E) in Berkeley, Northern California, is chosen to investigate the performance of the developed approach. Traffic data is obtained from the Performance Measurement System (PeMS).

ORCID iDs

Zhang, Liguo and Mao, Xuerong ORCID logoORCID: https://orcid.org/0000-0002-6768-9864;
  • Item type:Article
    ID code:52951
    Dates:
    Date
    Event
    27 September 2015
    Published
    19 April 2015
    Accepted
    Notes:This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory and Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library
    Subjects:Science > Mathematics > Probabilities. Mathematical statistics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:08 May 2015 15:29
    Last modified:11 Nov 2024 11:04
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/52951
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