Coordinated Intersection Signal Design for Mixed Traffic Flow of Human-Driven and Connected and Autonomous Vehicles

Qi, Hongsheng; Dai, Rumeng; Tang, Qing; Hu, Xianbiao · 2020 · OpenAlex-citations

DOI: 10.1109/access.2020.2970115

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Summary

This paper addresses the challenge of designing coordinated traffic signal systems for mixed traffic environments containing both Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). The authors argue that existing signal control methods, which assume fixed saturation flow rates and platoon dispersion behaviors typical of HDV-only traffic, are inadequate for mixed flows. CAVs exhibit significantly faster reaction times and more consistent speeds than HDVs, altering key macroscopic traffic characteristics: saturation flow rate and platoon dispersion. The study aims to develop a signal optimization model that accounts for these differences to improve intersection efficiency. To achieve this, the authors developed a modeling framework based on cumulative arrival and departure profiles across three defined locations: the entrance (detector location), the upstream intersection, and the downstream intersection. The model utilizes a combination of the Newell car-following model and the Akçelik acceleration model to derive the relationship between arrival and departure curves. Specifically, it simulates vehicle trajectories by accounting for distinct reaction delays (1.7 seconds for HDVs vs. 0.1 seconds for CAVs) and acceleration profiles. A mixed-flow platoon dispersion model was proposed to describe vehicle progression between intersections. Due to the nonlinear nature of the optimization problem, a Particle Swarm Optimization (PSO) algorithm was employed to determine optimal signal parameters, including cycle length, green duration, and offset. The study validated the model through a case study involving two intersections, with traffic demand simulated using a Markov chain. The analysis revealed distinct headway characteristics for the two vehicle types; stable headways for HDVs were approximately 2.25 seconds, while CAVs maintained headways around 0.6 seconds. The results demonstrated that the proposed model effectively reduces traffic delays compared to current signal control methods that do not account for mixed-flow dynamics. The study also calculated intersection capacity based on the weighted average of startup loss times for HDVs and CAVs, showing that the integration of CAVs alters the effective saturation flow rate. The significance of this work lies in providing a practical signal control strategy for the transitional period where HDVs and CAVs coexist. By explicitly modeling the impact of CAVs on saturation flow and platoon dispersion, the proposed approach offers a more accurate method for optimizing signal timing than traditional models. This contributes to the broader field of intelligent transportation systems by offering a pathway to maintain efficiency as autonomous vehicle penetration rates increase, without requiring immediate infrastructure upgrades for proactive CAV control.

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