Efficient multiple model particle filtering for joint traffic state estimation and incident detection

Ren Wang, Shimao Fan, Daniel B. Work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)521-537
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Volume71
DOIs
StatePublished - Oct 1 2016

Keywords

  • Field implementation
  • Multiple model
  • Particle filter
  • Traffic estimation
  • Traffic incident detection

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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