A new higher-order viscous continuum traffic flow model considering driver memory in the era of autonomous and connected vehicles

Lu Sun, Ammar Jafaripournimchahi, Alain Kornhauser, Wushen Hu

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This paper proposes a new car-following model by considering driver memory and derives a corresponding macroscopic continuum traffic flow model, in which the wrong-way travel phenomenon is not going to occur. We reveal that considering driver memory expressed in term of past traffic condition and headway leads to viscosity parameter in macroscopic traffic flow equation. The viscosity parameter is proportional to a unique quantity, which is featured with two parameters: the delay time of vehicle motion and the kinematic wave velocity at jam density. Linear and nonlinear stability analysis using the method of perturbation is carried out to study traffic characteristics. We showed that macroscopic models derived from microscopic models are more realistic and meaningful than those coming directly from an analogy of Navier–Stokes equations.

Original languageEnglish (US)
Article number123829
JournalPhysica A: Statistical Mechanics and its Applications
Volume547
DOIs
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

Keywords

  • Car-following model
  • Driver memory
  • Stability analysis
  • Viscous continuum traffic flow model

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