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

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Article number10.1109/TITS.2011.2178833
<mark>Journal publication date</mark>27/02/2012
<mark>Journal</mark>IEEE Transactions on Intelligent Transportation Systems
Issue number1
Number of pages13
Pages (from-to)36-48
Publication StatusPublished
Early online date4/01/12
<mark>Original language</mark>English


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.

Bibliographic note

Special Issue on Emergent Cooperative Technologies in Intelligent Transportation Systems