Traffic flow optimization using reinforcement and thread-based V2X communication for smart traffic management

Abstract

This research proposes an advanced framework that integrates reinforcement learning techniques with thread-based vehicle-to-everything (V2X) communication protocol to address urban traffic jams, which lead to increased delay, inefficient fuel consumption, and deteriorating air quality, posing a significant challenge to sustainable living. This framework empowers cars to “talk” directly to each other as well as with traffic signals, adapting to the real-time flow on the roads. By placing the decision-making process closer to where it’s needed, right at the network’s edge, the system is able to respond quickly to changes in traffic without relying on the usual pre-set signal cycles. Unlike many traditional traffic management solutions that are slow to adjust, the proposed framework allows vehicles and intersections to coordinate in real time. This reduces unnecessary waiting, smooths out traffic flow, and helps cut down on emissions. Through detailed simulations using the simulation of urban mobility (SUMO) traffic model, it was found that this system led to significant improvements: average waiting time was reduced by over 40%, queue lengths were cut by more than half, and vehicle throughput increased by nearly 30%. Additionally, fuel consumption dropped by approximately 24%, and carbon dioxide (CO2) emissions were reduced by more than 25% compared to existing systems.