Lancaster EPrints

Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems

Mihaylova, Lyudmila and Hegyi, A and Gning, Amadou and Boel, R. (2012) Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems. IEEE Transactions on Intelligent Transportation Systems, 13 (1). pp. 36-48. ISSN 1524-9050

PDF (IEEE_ITS_2012) - Draft Version
Download (848Kb) | Preview


    Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized.

    Item Type: Journal Article
    Journal or Publication Title: IEEE Transactions on Intelligent Transportation Systems
    Additional Information: Special Issue on Emergent Cooperative Technologies in Intelligent Transportation Systems
    Uncontrolled Keywords: particle filters ; Gaussian sum particle filtering ; traffic estimation
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 49617
    Deposited By: ep_importer_pure
    Deposited On: 07 Sep 2011 15:35
    Refereed?: Yes
    Published?: Published
    Last Modified: 26 May 2018 00:17
    Identification Number:

    Actions (login required)

    View Item