Role of Swarm Intelligence and Artificial Neural Network Methods in Intelligent Traffic Management

An increase in the volume of vehicles in metropolitan areas increases various issues, i.e., traffic jams, pollution, parking issues, noise, security, and accidental causes. Heavy traffic harm the social system because a large amount of time is squandered on it and then responsible for controlling the vehicle congestion problems is essential. An IoV (internet of Vehicles) innovation had also enhanced a wide range of applications associated with ITM. In this work, we are presenting an Intelligent traffic management system based on swarm intelligence and neural network. In the first phase, the proposed system applies a swarm intelligence method PSO for smart traffic light control. PSO detects the accurate cycle program for the management of traffic lights. In the second phase, an artificial neural network method will be applied to accurately detect and count the number of vehicles over the road. This recognition process mainly uses the roadway visual data and begins by extracting relevant the image mostly from the subsequent video sequence. As once context is recognized, consequent images have been used to identify objects moving through the object computation. The final phase applies the combined approach of phase-1 and phase-2 to achieves the best outcomes in terms of less congestion, less travel time, and better average speed.