Simulation Study on Comparative Analysis of Three Types of Signal Control Strategies at Intersections under Different Penetration Rates of Connected and Autonomous Vehicles
Year: 2026
Type: article
Abstract: During the urban traffic transition period characterized by the long-term mixed flow of human-driven vehicles (HV) and connected and automated vehicles (CAV), research lacks a systematic quantitative comparison of multiple signal control strategies across the full spectrum of CAV penetration rates.Based on the SUMO microscopic simulation platform, this paper constructs a standard urban four-way intersection model, calibrates the differentiated IDM carfollowing parameters for HV and CAV, sets up 6 levels of CAV penetration rate scenarios from 0% to 100%, and conducts full-factorial experiments on 3 mainstream signal control strategies: fixed timing, queue detection, and CAV green wave coordination. Average queue length, average delay, and average number of stops selected as core evaluation indicators, combined with the entropy weight method to complete a multi-indicator comprehensive performance evaluation, and the significance of the results is verified through paired ttests.The results show that: an increase in CAV penetration rate can significantly optimize intersection throughput efficiency, but marginal benefits diminish after the penetration rate exceeds 0%; queue-sensing control exhibits optimal robustness in the low-to-medium penetration rate range of 0%~40%, making it a cost-effective for the mixed-traffic transition period; CAV green wave coordinated control achieves optimal comprehensive performance when the penetration rate is ≥40%, whereas at low penetration rates, efficiency is inferior to sensing control due to idle V2X guidance resources;Fixed timing control performs the worst across all scenarios. A dynamic selection matrix for phased signal control strategies is constructed based on simulation conclusions, providing theoretical and engineering reference for the upgrading of signal control at urban intersections during different stages of intelligent connected vehicle penetration.
Author: Chen, Siqi
Institution: North China University of Science and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7223046982