Comparing traffic state estimators for mixed human and automated traffic flows

Ren Wang, Yanning Li, Daniel B. Work

Research output: Contribution to journalArticlepeer-review


This article addresses the problem of modeling and estimating traffic streams with mixed human operated and automated vehicles. A connection between the generalized Aw Rascle Zhang model and two class traffic flow motivates the choice to model mixed traffic streams with a second order traffic flow model. The traffic state is estimated via a fully nonlinear particle filtering approach, and results are compared to estimates obtained from a particle filter applied to a scalar conservation law. Numerical studies are conducted using the Aimsun micro simulation software to generate the true state to be estimated. The experiments indicate that when the penetration rate of automated vehicles in the traffic stream is variable, the second order model based estimator offers improved accuracy compared to a scalar modeling abstraction. When the variability of the penetration rate decreases, the first order model based filters offer similar performance.

Original languageEnglish (US)
Pages (from-to)95-110
Number of pages16
JournalTransportation Research Part C: Emerging Technologies
StatePublished - May 1 2017


  • Automated vehicles
  • Second order traffic flow model
  • Traffic state estimation

ASJC Scopus subject areas

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


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