Improving Traffic Flow: Intelligent Traffic Light Event Simulation by Integrating V2V and V2I Communication Models

Abstract

—Efficient traffic signal control is essential for reducing congestion, emissions and travel delays in modern urban environments. Traditional Vehicle-to-Infrastructure (V2I) systems are limited by infrastructure coverage, while Vehicle-to-Vehicle (V2V) communication alone lacks global signal-state awareness. This study proposes a hybrid V2V-V2I communication model that enhances Green Light Optimal Speed Advisory (GLOSA) performance by allowing vehicles to relay Signal Phase and Timing (SPaT) information in low-infrastructure or obstructed environments. A mathematical formulation describing vehicle motion, inter-vehicle message propagation and signal-state transitions is developed, complemented by a detailed algorithmic description of the hybrid control logic. The model is implemented using SUMO with the Krauss microscopic car-following model to simulate real-world driving behavior. Results demonstrate that the hybrid model reduces stop frequency, travel time, fuel consumption, and CO₂ emissions compared to standalone V2I and V2V schemes. The added V2V relaying mechanism improves prediction accuracy, particularly in scenarios with limited roadside units. The findings highlight the feasibility, scalability and ecological benefits of integrating multi-source communication in intelligent traffic systems, offering a promising solution for future connected urban mobility.