An anisotropic traffic flow model with look-ahead effect for mixed autonomy traffic

TL;DR

This paper extends a traffic flow model to include the look-ahead capabilities of autonomous vehicles, analyzing their impact on traffic stability and convergence in mixed traffic environments.

Contribution

It introduces a non-local look-ahead effect into the ARZ traffic model and analyzes how it influences stability and convergence in mixed autonomous and human-driven vehicle traffic.

Findings

Longer look-ahead distances loosen stability criteria.

Increased CAV market penetration stabilizes mixed traffic.

Spatial distribution of CAVs has less impact on stability.

Abstract

In this paper we extend the Aw-Rascle-Zhang (ARZ) non-equilibrium traffic flow model to take into account the look-ahead capability of connected and autonomous vehicles (CAVs), and the mixed flow dynamics of human driven and autonomous vehicles. The look-ahead effect of CAVs is captured by a non-local averaged density within a certain distance (the look-ahead distance). We show, using wave perturbation analysis, that increased look-ahead distance loosens the stability criteria. Our numerical experiments, however, showed that a longer look-ahead distance does not necessarily lead to faster convergence to equilibrium states. We also examined the impact of spatial distributions and market penetrations of CAVs and showed that increased market penetration helps stabilizing mixed traffic while the spatial distribution of CAVs have less effect on stability. The results revealed the potential of using CAVs to stabilize traffic, and may provide qualitative insights on speed control in the mixed autonomy environment.