Trajectory Optimization of Connected and Autonomous Vehicles (CAVs) at Signalized Intersections

Fan, Wei (David); Liu, Pengfei · 2020 · 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 the efficiency of signalized intersections, aiming to quantify performance improvements and establish guidelines for traffic engineering under varying CAV market penetration levels. The research is motivated by the potential of CAV technologies, specifically Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications, to reduce travel delays, fuel consumption, and emissions by enabling vehicles to coordinate maneuvers and avoid stopping at traffic signals. The study focuses on a mixed traffic environment comprising regular vehicles, Autonomous Vehicles (AVs), and CAVs to reflect realistic future scenarios. The methodology employs a microscopic traffic simulation using VISSIM, centered on a case study of a signalized intersection in Charlotte, North Carolina. Historical traffic volume data and signal plans were collected to configure the simulation environment. To accurately model CAV behavior, the researchers adjusted driving parameters such as standstill distance and minimum headway. Since VISSIM’s internal driver model cannot simulate CAV operations, a speed advisory strategy was implemented via a Python script using VISSIM’s Component Object Model (COM) interface. This strategy calculates optimal speeds for CAVs to ensure they arrive at the intersection during a green phase, thereby optimizing their trajectories. The results demonstrate that the proposed speed advisory strategy effectively reduces vehicle stops, travel delay, and environmental impacts. The study quantifies these benefits across different CAV penetration rates, showing improvements in traffic delay, stopped delay, average and maximum queue lengths, and emissions of CO, NOx, and VOCs, as well as fuel consumption. The findings indicate that intersection efficiency improves as CAV penetration increases, with the optimized trajectories allowing CAVs to pass through intersections without stopping. The simulation also highlights the distinct performance differences between regular vehicles, AVs, and CAVs, providing a detailed analysis of how mixed traffic flows are affected by the introduction of connected technologies. The significance of this research lies in its contribution to the development of guidelines for estimating intersection efficiency in the presence of CAVs. By providing empirical evidence on how different market penetration levels influence traffic operations, the study offers valuable insights for traffic engineers and stakeholders. It supports better planning and operational decisions for signalized intersections, facilitating the integration of CAV technologies into existing transportation infrastructure. The findings underscore the potential of CAVs to enhance mobility, safety, and environmental sustainability, providing a foundation for future research and implementation strategies in smart transportation systems.

Key finding

Optimizing CAV trajectories through speed advisory strategies reduces travel delay, vehicle stops, and emissions at signalized intersections, with efficiency gains increasing as CAV market penetration rates rise.

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