Integrating V2V and V2I Communications for Enhanced Traffic Safety and Efficiency in Connected Vehicular Networks

This paper presents a new approach to improve road safety and traffic flow by combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Our study focuses on a system that connects vehicles with each other and with traffic signals to share real-time data about speed, position, and road conditions. Using both analytical models and experimental simulations built in MATLAB/Simulink, we developed a system that predicts and advises the optimal speed for vehicles approaching an intersection. In our design, a Green Light Optimized Speed Advisory (GLOSA) feature suggests an ideal speed for drivers so that they can pass through green lights without unnecessary stopping. This not only improves traffic flow but also reduces fuel consumption and lowers the risk of accidents. We validated our system through a series of simulations and real-time traffic scenarios. The results show that our approach can adapt dynamically to changes in vehicle behavior and traffic density. In high-traffic situations, the system adjusts signal timings and advising speeds to maintain smooth flow. Our experiments confirmed significant improvements in reducing stop-and-go driving and in enhancing intersection throughout compared to traditional traffic control methods. The originality of our work lies in the integration of both V2V and V2I communications within a scalable framework, combined with a robust speed advisory algorithm. This work paves the way for smarter, more responsive traffic management systems and supports the future deployment of connected and autonomous vehicles.