Intelligent traffic light control for congestion management for smart city development

Smart City development centres around efficient resource management along with sustainable support to the environment. This paper presents methods for intelligent control of traffic lights for traffic management and resolving road congestion incidents. The increasing volume of traffic, along with ineffective management of road capacity, has contributed towards increasing number of congestion events on road networks. The congestion events may be managed through control of the sequence in which traffic lights turn green, along with modifying the time for which the lights are green for each phase. In this paper, optimal traffic light sequence is obtained for a single intersection (node) using the Hopfield Neural Network (HNN). The optimal green time (g_i) for the traffic lights is obtained using Genetic Algorithm (GA) for a 4 phase road network. It was observed that the HNN provides the optimal sequence of green lights in an average of 16 iterations. GA provides a value of g_i that would maximise the traffic flow. Results of optimal g_i are presented for various cycle times. It is found that the flow rate increases with the increase in green times (g_i).