Neuro-Adaptive Traffic Congestion Control for Urban Road Networks

The rapid increase of private vehicles combined with the limited capabilities of the urban road infrastructure has made congestion one of the main problems of major cities worldwide, having a severe impact on both the economy and the environment. In this work, we shall attempt to solve the traffic management problem by examining in a unified manner the traffic network, the route guidance of the vehicles and the regulation of the traffic lights, as the basic elements of a single controlled system. In particular, we propose a decentralized adaptive control system, comprised of three main modules: i) the network congestion estimator, ii) the reference travel time estimator, and iii) the rate controller, that is capable of efficiently regulating the travel time along the traffic network while avoiding congestion at the junctions. The design of decentralized control algorithms and their implementation as traffic management applications for portable computing devices (e.g., 3rd and 4th generation mobile phones, tablets, computers embedded in "smart" vehicles) is expected to improve drastically the traffic condition of urban road networks. Meanwhile, in future traffic networks, where the navigation of the vehicles will be conducted by autopilots in the absence of human-drivers, the use of such a distributed autonomous management system will be essential.