From Micro to Macro Flow Modeling: Characterizing Heterogeneity of Mixed-Autonomy Traffic

TL;DR

This paper introduces a novel method to characterize and validate heterogeneity in mixed-autonomy traffic flow by leveraging Lagrangian vehicle trajectory data to inform macroscopic models, improving accuracy and generalization.

Contribution

It develops a continuous traffic-heterogeneity attribute and a reconstruction method to connect microscopic trajectories with macroscopic flow models, addressing data scarcity and validation challenges.

Findings

Effectively captures traffic heterogeneity through attribute-based clustering.

Reduces calibration errors of flow models by 20%.

Maintains high accuracy across unseen traffic conditions.

Abstract

Most autonomous-vehicles (AVs) driving strategies are designed and analyzed at the vehicle level, yet their aggregate impact on macroscopic traffic flow is still not understood, particularly the flow heterogeneity that emerges when AVs interact with human-driven vehicles (HVs). Existing validation techniques for macroscopic flow models rely on high-resolution spatiotemporal data spanning entire road segments which are rarely available for mixed-autonomy traffic. AVs record detailed Lagrangian trajectories of the ego vehicle and surrounding traffic through onboard sensors. Leveraging these Lagrangian observations to validate mixed-autonomy flow models therefore remains an open research challenge. This paper closes the gap between microscopic Lagrangian data and macroscopic Euclidean traffic models by introducing a continuous traffic-heterogeneity attribute. We represent traffic flow with two coupled conservation laws with one for vehicle number and one for the traffic attribute. Reconstruction methods are designed to derive the traffic attribute from Lagrangian vehicle trajectories. When abundant trajectory data are available, we characterize traffic heterogeneity by extracting drivers' desired speed and local behavioral uncertainty from trajectories. In data-scarce mixed traffic, we design an end-to-end mapping that infers the traffic heterogeneity solely from trajectories in the current spatiotemporal region. Experiments across multiple traffic datasets show that the proposed model effectively captures traffic heterogeneity by clustering the fundamental diagram scatter into attribute-based groups. The calibration errors of traffic flow dynamics are also reduce by 20% relative to the Aw-Rascle-Zhang model benchmark. Detailed analyses further show that the model generalizes well, maintaining nearly the same accuracy when evaluated under a variety of previously unseen traffic conditions.