Impact of Connected and Autonomous Vehicles on Signalized Intersections With Transit Signal Priority

Fan, Wei; Yang, Tianjia · 2022 · ROSA P / University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education

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Summary

This study investigates the impact of Connected and Autonomous Vehicles (CAVs) on signalized intersections utilizing Transit Signal Priority (TSP). While TSP is a critical strategy for improving transit performance, its widespread adoption is often limited by the adverse effects it imposes on general traffic. The authors posit that CAV technology, specifically its capability for real-time data exchange between vehicles and infrastructure, can facilitate more efficient TSP control strategies that balance transit priority with overall traffic flow. The research aims to evaluate the traffic performance of two CAV-enabled TSP strategies—actuated TSP with Connected Vehicles (CV) and optimized TSP with CV—against conventional signal control methods. The methodology employs a simulation-based experimental design centered on a real-world signalized intersection in Charlotte, North Carolina. The study utilizes a Genetic Algorithm (GA) to develop an optimized TSP control strategy aimed at minimizing total person delay, considering both queuing delay and delay for approaching vehicles. The simulation framework tests various scenarios differing in CAV market penetration rates, traffic demand levels, and bus arrival frequencies. The performance of the proposed GA-optimized strategy and the fully actuated TSP with CV strategy is compared against baseline scenarios involving actuated signal control with and without TSP. The results demonstrate that the GA-optimized control strategy significantly reduces average bus delay by 24.50% while minimizing negative impacts on competing traffic under high-demand conditions. Conversely, the fully actuated control with TSP using CV technology yields the best average delay performance under low traffic demand conditions. This latter strategy is noted for its practicality, as it requires only bus equipment with CV technology, which is low-cost and easily implementable. Furthermore, the optimized algorithm provides priority benefits to buses even at low rates of CV market penetration. Sensitivity analyses reveal that the proposed optimization algorithm is not highly sensitive to variations in bus occupancy or bus arrival frequency, indicating robust performance across different operational conditions. The significance of this research lies in providing a systematic reference for researchers and practitioners to plan, design, and operate TSP strategies within a CAV environment. By demonstrating that CAV-enabled TSP can mitigate the traditional trade-off between transit priority and general traffic efficiency, the study supports the integration of these technologies to create more sustainable, equitable, and efficient urban transportation systems. The findings suggest that even partial adoption of CAV technology can yield measurable improvements in intersection performance, offering a viable pathway for incremental implementation of advanced traffic control strategies.

Key finding

The proposed Genetic Algorithm optimization control strategy reduces average bus delay by 24.50% under high traffic demand conditions while minimizing adverse impacts on competing traffic.

Methodology

simulator

Provenance

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

Topics

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• v2x connected vehicle