Multiple model particle filter for traffic estimation and incident detection

Ren Wang, Daniel B. Work, Richard Sowers

Research output: Contribution to journalArticlepeer-review


This paper poses the joint traffic state estimation and incident detection problem as a hybrid state estimation problem, in which a continuous variable denotes the traffic state and a discrete model variable identifies the location and severity of an incident. A multiple model particle smoother is proposed to solve the hybrid estimation problem, in which the multiple model particle filter is used to accommodate the nonlinearity and switching dynamics of the traffic incident model, and the smoothing algorithm is applied to improve the accuracy of the estimate when data are limited. The proposed algorithms are evaluated through numerical experiments using CORSIM as the true model. The proposed algorithm is also compared with a standard macroscopic traffic estimator via particle filtering and the California incident detection algorithm. The results show that jointly estimating the state and incidents in one algorithm is better than two dedicated algorithms working independently.

Original languageEnglish (US)
Article number7485898
Pages (from-to)3461-3470
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number12
StatePublished - Dec 2016


  • Particle filtering
  • traffic estimation
  • traffic incident detection

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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