Improving the performance of unsignalized t-intersections within CAVs mixed traffic

Alanazi, Fayez; Yi, Ping; El, Gehawi · 2022 · Crossref

DOI: 10.5937/jaes0-34023

archive: archived pipeline: cataloged verified

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Summary

This study addresses the challenge of traffic congestion and safety at unsignalized T-intersections, particularly where minor road vehicles must yield to major road traffic. The authors identify that limited gap availability on the major road forces minor road drivers to wait excessively or accept unsafe gaps, leading to delays and increased collision risks. Motivated by the rise of Connected and Automated Vehicles (CAVs), the research aims to develop a control algorithm that utilizes CAVs on the major road to create additional, usable gaps for minor road vehicles. The study specifically focuses on mixed traffic conditions, where CAVs coexist with human-driven vehicles (HVs), rather than fully automated environments. The methodology involves developing a systematic framework for CAVs to adjust their speed to generate safe gaps. The system relies on Vehicle-to-Infrastructure (V2I) communication via a Roadside Unit (RSU) to detect vehicles on the minor road and transmit this information to CAVs on the major road. When a minor road vehicle is detected, the CAV calculates the current gap time relative to the critical gap required for safe entry. If the gap is insufficient, the CAV evaluates whether it can safely reduce speed to extend the gap. This decision is governed by safety constraints, including the distance to the leading vehicle and the safe car-following distance (CFD) to the following vehicle, calculated using Stopping Sight Distance principles. The algorithm ensures that speed reductions do not compromise the safety of the major road stream or cause rear-end collisions. The proposed framework was validated through microscopic simulations. The simulation results demonstrate that the control algorithm significantly improves intersection performance. Specifically, the delay and queue length for the minor road approach were minimized without causing significant delays to the mainline traffic. The study found that when the penetration rate of CAVs on the major road reached 70%, the delay for minor road vehicles was reduced by 72% compared to a benchmark scenario with no CAVs. The algorithm successfully balanced efficiency and safety by creating extra gaps only when conditions permitted, thereby reducing interruptions to the major road flow while facilitating smoother merging for minor road traffic. The significance of this research lies in its contribution to the management of mixed traffic environments during the transitional phase of CAV adoption. By proving that partial CAV penetration can substantially enhance the efficiency and safety of unsignalized intersections, the study provides a practical application for Intelligent Transportation Systems. The findings suggest that leveraging CAVs for gap creation is a viable strategy to mitigate congestion and reduce collision risks at intersections where traditional signalization is not present or effective. This approach offers a pathway to improve traffic flow dynamics and reduce fuel consumption and emissions in suburban and urban areas with mixed vehicle fleets.

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• traffic density