Optimal and Robust Control of Vehicle Platooning on Signalized Arterial with Significant Freight Traffic

Xiao, Xiao; Zhang, Yulong; Wang, Bruce · 2021 · ROSA P / Freight Mobility Research Institute. Florida Atlantic University

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Summary

This study addresses the challenge of maintaining traffic stability and throughput on signalized arterials with significant freight traffic, where the interaction between passenger cars and trucks often leads to instability and bottlenecks. While previous research has extensively analyzed the stability parameters of car-following models in mixed traffic, few studies have applied theoretical stability conditions to actively control Automated Vehicles (AVs) to improve mobility while meeting throughput demands. The authors aim to develop an adaptive headway control method for AVs that ensures string stability in heterogeneous traffic composed of human-driven vehicles (HVs), AVs, cars, and trucks. The methodology employs the Intelligent Driver Model (IDM) to formulate generalized stability conditions for mixed traffic. The authors derive stability regions based on vehicle parameters such as maximal acceleration, comfortable deceleration, and desired headway, accounting for different penetration rates of trucks and AVs. The core innovation is an adaptive control strategy where AVs dynamically adjust their desired headways in real-time based on current equilibrium speed and traffic demand. This approach ensures that the chosen headway satisfies both the mathematical stability condition and the throughput requirement (inverse of flow demand). The system design was validated using VISSIM microsimulation, comparing the proposed adaptive method against a baseline scenario where AVs maintained a fixed desired headway. The stability analysis revealed that traffic stability is highly sensitive to the proportion of trucks. Specifically, traffic can only remain stable if the truck ratio is less than 80%; beyond this threshold, it becomes impossible to satisfy both stability and throughput requirements simultaneously. When the truck ratio is moderate (around 60%), the adaptive adjustment of AV headways becomes critical for maintaining stability without sacrificing flow. Simulation results demonstrated that the proposed adaptive method significantly outperformed the fixed-headway baseline. Under downstream perturbations, the adaptive control prevented the propagation of oscillations upstream, thereby stabilizing the traffic flow. Quantitatively, the method reduced travel delay by 23.19% and increased average speed by 9.09% compared to the baseline. The significance of this work lies in its practical application of theoretical stability analysis to AV control logic in mixed-traffic environments. By linking AV headway adjustments to real-time stability conditions, the study provides a robust framework for improving mobility on freight-heavy corridors. The findings highlight the critical threshold of truck penetration for stability and offer a validated control strategy that enhances efficiency and safety. This approach supports the integration of Connected and Automated Vehicles (CAVs) into existing infrastructure, offering a pathway to mitigate the destabilizing effects of heavy freight traffic on urban arterial networks.

Key finding

The adaptive headway method reduced delay by 23.19% and increased average speed by 9.09% compared to a baseline with fixed headways, while traffic stability was only possible when truck ratios were less than 80%.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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