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A Particle Filter for Freeway Traffic Estimation

Mihaylova, L. and Boel, R. (2004) A Particle Filter for Freeway Traffic Estimation. Proc. of the 43rd IEEE Conf. on Decision and Control, 2. pp. 2106-2111. ISSN 0191-2216

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    Abstract

    This paper considers the traffic flow estimation problem for the purposes of on-line traffic prediction, mode detection and ramp-metering control. The solution to the estimation problem is given within the Bayesian recursive framework. A particle filter (PF) is developed based on a freeway traffic model with aggregated states and an observation model with aggregated variables. The freeway is considered as a network of components, each component representing a different section of the traffic network. The freeway traffic is modelled as a stochastic hybrid system, i.e. each traffic section possesses continuous and discrete states, interacting with states of neighbor sections. The state update step in the recursive Bayesian estimator is performed through sending and receiving functions describing propagation of perturbations from upstream to downstream, and from downstream to upstream sections. Measurements are received only on boundaries between some sections and averaged within regular or irregular time intervals. A particle filter is developed with measurement updates each time when a new measurement becomes available, and with possibly many state updates in between consecutive measurement updates. It provides an approximate but scalable solution to the difficult state estimation and prediction problem with limited, noisy observations. The filter performance is validated and evaluated by Monte Carlo simulation.

    Item Type: Article
    Journal or Publication Title: Proc. of the 43rd IEEE Conf. on Decision and Control
    Additional Information: IEEE Catalog number: 04CH37601C, ISBN: 0-7803-8683-3 "©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
    Uncontrolled Keywords: Monte Carlo methods ; Bayesian estimation ; particle filters ; macroscopic traffic models ; stochastic hybrid systems ; DCS-publications-id ; inproc-428 ; DCS-publications-credits ; dsp-fa ; DCS-publications-personnel-id ; 121
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 826
    Deposited By: Dr L Mihaylov
    Deposited On: 11 Jan 2008 10:20
    Refereed?: Yes
    Published?: Published
    Last Modified: 17 Sep 2013 08:24
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/826

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