Real-Time Traffic Management and Safety Enhancements in Internet of Vehicles Using Deep Reinforcement Learning

Internet of Vehicles (IoV) has transformed the modern transportation systems to include effective real time communication between the vehicles, infrastructures and management of traffic systems. Yet, the effective planning of vehicular traffic as well as safety in such conditions is a complex problem that even today cannot be solved satisfactorily. This paper aims to present deep reinforcement learning (DRL) approaches to tackle these challenges with regard to IoV networks, particularly in real time traffic control and safety improvement. Our approach presented here uses the concept of DRL to create an autonomous control system that regulates traffic based on the traffic density, dynamics, or patterns. The proposed structure incorporates multiple agents in DRL to control the signal timings at intersection, vehicle routing, and forecast any collisions. Safe changes are made possible by using actual traffic data from vehicle sensors and structures to make preventive decisions against mishaps. This paper shows that computational simulations can predict that traffic flow productivity has increased significantly through decreases in average time per vehicle, fuel usage and emissions. Furthermore, safety metrics show that collisions probabilities have drastically reduced. The proposed system is therefore scalable and robust and can best be suited for application in the urban and highway environments. Thus, this work demonstrates tangible possibilities in the application of DRL in improving the IoV systems for safer and intelligent transport systems that support development of smarter cities and sustainable mobility.