Vehicle density estimation of freeway traffic with unknown boundary demand-supply: an IMM approach

Liguo Zhang, Xuerong Mao

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
166 Downloads (Pure)

Abstract

As distributed parameter systems, dynamics of freeway traffic are dominated by the current traffic parameter and boundary fluxes from upstream/downstream sections or on/off ramps. The difference between traffic demand-supply and boundary fluxes actually reflects the congestion level of freeway travel. This paper investigates simultaneous traffic density and boundary flux estimation with data extracted from on-road detectors. The existing studies for traffic estimation mainly focus on the traffic parameters (density, velocity, etc.) of mainline traffic and ignore flux fluctuations at boundary sections of the freeway. We propose a stochastic hybrid traffic flow model by extending the cell transmission model (CTM) with Markovian multi-mode switching. A novel interacting multiple model (IMM) filtering for simultaneous input and state estimation is developed for discrete-time Markovian switching systems with unknown input. A freeway segment of Interstate 80 East (I-80E) in Berkeley, Northern California, is chosen to investigate the performance of the developed approach. Traffic data is obtained from the Performance Measurement System (PeMS).
Original languageEnglish
Pages (from-to)1989-1995
Number of pages8
JournalIET Control Theory and Applications
Volume9
Issue number13
DOIs
Publication statusPublished - 27 Sep 2015

Keywords

  • Lighthill-Whitham and Richards (LWR)
  • stochastic hybrid traffic model
  • simultaneous input and state estimation
  • IMM filter
  • boundary flow

Fingerprint Dive into the research topics of 'Vehicle density estimation of freeway traffic with unknown boundary demand-supply: an IMM approach'. Together they form a unique fingerprint.

Cite this