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

TL;DR

The paper addresses stabilizing mixed-autonomy traffic by extending the ARZ model with a nonlocal look-ahead density $\rho^*(L_D)$ to represent CAV sensing, and analyzes stability using wave perturbation. The approach shows that the stability criterion loosens as the look-ahead distance $L_D$ increases, though longer look-ahead does not always yield faster convergence, and it extends the framework to a multiclass HDV/CAV setting to study penetration and distribution effects via numerical experiments. Key findings reveal that moderate look-ahead can outperform full-road observation for convergence speed and that higher CAV penetration generally stabilizes mixed traffic, while the spatial distribution of CAVs has a limited impact on long-term stability. Collectively, the results provide qualitative guidance for speed-control strategies in mixed autonomy traffic and highlight the nuanced role of look-ahead sensing in stabilization outcomes.

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.

Figures (11)

• Figure 1: A CAV's front observation of the traffic density of a certain distance in front.
• Figure 2: Density and velocity evolution of the ARZ model ($L_D\to 0^+$), where the flow is not stable.
• Figure 3: Density and velocity evolution of the modified model with $L_D=15$m.
• Figure 4: Density and velocity evolution of the modified model with $L_D=100$m.
• Figure 5: Density and velocity evolution of the modified model with $L_D=1000$m.

Theorems & Definitions (4)

• Remark 1
• Remark 2
• Remark 3
• Remark 4