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

Research output: Contribution to journalArticle

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
DOIs
StateAccepted/In press - Jan 1 2020

Fingerprint

Traffic Flow Model
traffic
Driver
Viscosity
vehicles
Continuum
Traffic
Higher Order
continuums
Car-following Model
Nonlinear Stability
Delay Time
Linear Stability
Traffic Flow
Nonlinear Analysis
Analogy
Two Parameters
Stability Analysis
Kinematics
Navier-Stokes Equations

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Statistics and Probability

Keywords

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

Cite this

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