Mihaylova, L., Hegyi, A., Gning, A. et al. (1 more author) (2012) Parallelized Particle and Gaussian Sum Particle Filters for Large-Scale Freeway Traffic Systems. IEEE Transactions on Intelligent Transportation Systems, 13 (1). 36 - 48. ISSN 1524-9050
Abstract
Large-scale traffic systems require techniques that are 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, and 4) cope with multimodal conditional probability density functions (pdfs) for the states. Often, centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques that are 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 online traffic management. We show how complex pdfs of the high-dimensional traffic state can be decomposed into functions with simpler forms and how the whole estimation problem solved in an efficient way. The proposed approach is general, with limited interactions, which reduce the computational time and provide high estimation accuracy. The efficiency 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Freeway traffic state estimation; high-dimensional systems; multimodality; parallelized Gaussian sum particle filters (PGSPFs); parallelized particle filters (PPFs) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Dec 2014 16:32 |
Last Modified: | 04 Nov 2016 06:54 |
Published Version: | http://dx.doi.org/10.1109/TITS.2011.2178833 |
Status: | Published |
Publisher: | IEEE |
Refereed: | No |
Identification Number: | 10.1109/TITS.2011.2178833 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82268 |