Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data

2026-08-01

AI Summary

This study addresses the limitations of conventional lane-based traffic flow models in capturing chaotic traffic conditions characterized by the absence of marked lanes, high heterogeneity, and persistent lateral interactions. Leveraging high-resolution drone trajectory data, it integrates a two-dimensional Edie macroscopic framework with microscopic car-following analysis to construct, for the first time, a two-dimensional macroscopic fundamental diagram tailored to unlaned mixed traffic. The work reveals the critical role of lateral redistribution in congestion propagation and quantifies temporal headways and lateral spacing distributions across vehicle types through spatiotemporal speed fields, identification of steady-state car-following relationships, and explicit modeling of vehicular heterogeneity. These advances enable accurate characterization of stop-and-go wave dynamics and provide a data-driven foundation for calibrating and validating unlaned traffic flow models.

Abstract

Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous lateral interactions, challenging traditional lane-based modeling assumptions. This study presents an empirical study of macroscopic and microscopic aspects of disordered traffic using high-resolution UAV trajectory data collected on an urban arterial. A two-dimensional extension of Edie's framework is applied to quantify aggregate traffic variables and produce a two-dimensional fundamental diagram, revealing that traffic states cannot be adequately represented using one-dimensional formulations and highlighting the persistent role of lateral redistribution. The propagation of congestion is estimated directly from the spatiotemporal speed fields, demonstrating the emergence of coherent stop-and-go waves and showing a similar dynamics as conventional lane-based flow, in spite of the heterogeneous vehicle interactions. At the microscopic level, steady-state follower-leader identification is used to examine desired time gaps and minimum lateral spacing, vehicle dimension distributions, and kinematic characteristics, revealing pronounced inter-class heterogeneity that explains disordered traffic behavior. The study provides an empirical framework linking vehicle-level interactions and aggregate traffic dynamics and establishes a data-driven basis for the calibration and validation of traffic models for disordered mixed traffic systems.

Problem

• disordered traffic
• lane discipline
• vehicle heterogeneity
• lateral interactions
• traffic flow dynamics

Innovation

• disordered traffic
• two-dimensional fundamental diagram
• high-resolution trajectory data
• vehicle heterogeneity
• lateral interactions