Vehicular Networking for Intelligent and Autonomous Traffic Management

Traffic congestion has become a daily problem that most people suffer. This not only impacts the productivity of the population but also poses a safety risk. Most of the technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the traffic flow. This paper presents a new approach for enabling autonomous and adaptive traffic management through vehicular networks. By allowing data exchange between vehicles about route choices, congestions and traffic alerts, a vehicle makes a decision on the best course of action. Unlike centralized schemes that provide recommendations, our VANET-based Autonomous Management (VAM) approach factors in the destination and routes of nearby vehicles in deciding on whether rerouting is advisable. In addition, VAM leverages the presence of smart traffic lights and enables coordination between vehicles and light-controllers in order to ease congestion. The collective effect of all vehicles will be an autonomous reshape of the traffic pattern based on their destinations and road conditions. The simulation results demonstrate the advantage of VAM.