Efficient multiple model particle filtering for joint traffic state estimation and incident detection. (October 2016)

• Record Type: Journal Article
• Authors: Wang, Ren; Fan, Shimao; Work, Daniel B.
• Is Part Of: Transportation research. Volume 71(2016)
• Page Start: 521
• Page End: 537
• Publication Date: 2016-10
• Subjects: Traffic estimation -- Traffic incident detection -- Multiple model -- Particle filter -- Field implementation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.08.003
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: An algorithm is proposed for traffic estimation and incident detection. The method is tested on benchmarks problems, in micro simulation, and on field data. The method is faster than other filters when the number of possible incidents grows. Abstract: This article proposes an efficient multiple model particle filter (EMMPF) to solve the problems of traffic state estimation and incident detection, which requires significantly less computation time compared to existing multiple model nonlinear filters. To incorporate the on ramps and off ramps on the highway, junction solvers for a traffic flow model with incident dynamics are developed. The effectiveness of the proposed EMMPF is assessed using a benchmark hybrid state estimation problem, and using synthetic traffic data generated by a micro-simulation software. Then, the traffic estimation framework is implemented using field data collected on Interstate 880 in California. The results show the EMMPF is capable of estimating the traffic state and detecting incidents and requires an order of magnitude less computation time compared to existing algorithms, especially when the hybrid system has a large number of rare models.