An Information Extraction Approach with Vehicle and Pedestrian Activity Monitoring For Traffic Management in Smart City

Traffic congestion is one of the main concerns of big cities for traffic management. Among different dimensions that make a city smart, one of the very important is transportation. This paper offers an efficient information extraction approach from vehicle and pedestrian activity monitoring for traffic management in the smart city. The proposed approach is related to vehicle and pedestrian activity recognition and traffic optimization in order to manage traffic in smart. The traffic management system includes implementation of Particle Swarm Optimization algorithm and Fuzzy Logic Controller in the solution of the traffic signal timing optimization. Moreover, activity recognition is performed to detect traffic lights that are in wrong place, missing and unnecessary. It has been interested not only in activity recognition of vehicles but also in activity recognition of pedestrians in this work. The implementation of this method does not entail complex equipment and feasibility of this proposed method is quite high.