Learning Traffic Flow Dynamics Using Random Fields

Saif Eddin G. Jabari, Deepthi Mary Dilip, Dianchao Lin, Bilal Thonnam Thodi

Research output: Contribution to journalArticle

Abstract

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy.

Original languageEnglish (US)
Article number8835040
Pages (from-to)130566-130577
Number of pages12
JournalIEEE Access
Volume7
DOIs
StatePublished - Jan 1 2019

Keywords

  • conditional random fields
  • factor graphs
  • Markov random fields
  • Stochastic traffic dynamics
  • traffic state estimation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'Learning Traffic Flow Dynamics Using Random Fields'. Together they form a unique fingerprint.

  • Cite this

    Jabari, S. E. G., Dilip, D. M., Lin, D., & Thonnam Thodi, B. (2019). Learning Traffic Flow Dynamics Using Random Fields. IEEE Access, 7, 130566-130577. [8835040]. https://doi.org/10.1109/ACCESS.2019.2941088