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

Boel, R.

Gning, Amadou

Hegyi, A

Mihaylova, Lyudmila

English

Lancaster E-Prints

A comparison of ﬁlter conﬁgurations for freeway trafﬁc state estimation,” in

A Monte Carlo approach to nonnormal and non-linear state-space modelling,”

A tutorial on particle ﬁlters for on-line non-linear/non-Gaussian Bayesian tracking,”

An adaptive freeway trafﬁc state estimator and its real-data testing—Part I: Basic properties,” in

An adaptive freeway trafﬁc state estimator and its real-data testing—Part II: Adaptive capabilities,” in

An efﬁcient optimization approach to real-time coordinated and integrated freeway trafﬁc control,”

C.Coates,“Distributed particleﬁltering forsensor networks,” in

Continuum trafﬁc ﬂow modelling of cooperative trafﬁc systems,”

Data fusion in intelligent transportation systems: Progress and challenges—A survey,”

Decentralized state initialization with delay compensation for multimodal sensor networks,”

Distributed particle ﬁlter for target tracking,” in

Distributed particle ﬁlter with GMM approximation for multiple targets localization and tracking in wireless sensor network,” in

Distributed tracking of stealthy targets using particle ﬁlters,” in

Eds., Sequential Monte Carlo Methods in Practice.

Efﬁcient Gaussian mixture ﬁlter for hybrid positioning,” in

Efﬁcient routing on large road networks using hierarchical communities,”

Freeway trafﬁc estimation within recursive

Freeway trafﬁc management and control,”

Gaussian mixture ﬁlter and hybrid positioning,” in

Gaussian particle ﬁltering,”

Gaussian sum particle ﬁltering for dynamic state space models,” in

Gaussian sum particle ﬁltering,”

Hierarchical modelbased predictive control for intelligent vehicle highway systems: Regional controllers,” in

Ing,“Distributed particle ﬁlters for object tracking in sensor networks,”

Mixture Kalman ﬁlter based highway congestion mode and vehicle density estimator and its application,” in

Multiple particle ﬁltering,” in

Nonlinear ﬁltering using a new proposal distribution and the improved fast gauss transform with tighter performance bounds,”

Online learning solutions for freeway travel time prediction,”

Optimal coordination of variable speed limits to suppress shock waves,”

Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,”

Parallelised Gaussian mixture ﬁltering for vehicular trafﬁc ﬂow estimation,”

Parallelized particle ﬁltering for freeway trafﬁc state tracking,” in

Real-time data fusion of road trafﬁc and ETC data for road network monitoring,” in

Real-time freeway trafﬁc state estimation based on extended Kalman ﬁlter: A general approach,”

Resampling algorithms and architectures for distributed particle ﬁlter,”

Robust control of trafﬁc networks under uncertain conditions,”

Sequential Monte Carlo methods fory dynamical system,”

Study on fast model predictive controllers for large urban trafﬁc networks,” in

Target tracking based on a distributed particle ﬁlter in underwater sensor networks,”

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

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

Mihaylova, L.

Hegyi, A.

Gning, A.

Boel, R.K.

White Rose Research Online

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

http://eprints.lancs.ac.uk/49617/1/IEEE_ITS_2012.pdf

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

Abstract

Similar works

Full text

Available Versions

Lancaster E-Prints

White Rose Research Online