Analysis of traffic congestion in main streets of electronic city using traffic congestion index and artificial neural network (case study: Hamedan city)

Smart cities are a kind of umbrellas of different technologies for responding to the problem of increasing urban population. The priority of intelligent electronic cities is a strategy to collecting information about the city and its smart use to improve the provided services to citizens or to create new services. These smart cities have weather forecast, urban monitoring, pollution monitoring and various applications. Traffic is a major challenge for electronic cities and coping with it requires analyzing traffic congestion in the city road network. The data transmission with wireless signals in smart cities is one of the challenges because construction of high buildings and barriers reduces the power and quality of the signal. Widespread use of wireless signals and equipment may lead to interference and reduce service quality. Therefore, in order to solve the traffic problem, it is necessary to achieve traffic congestion levels by collecting information, especially with wireless signals so that it can be programmed to control and manage traffic. In this paper, the performance index of vehicle speed was estimated to evaluate the conditions of road networks. This study analyzes the traffic density for the main network of Hamedan communication routes based on the collected data of Speed performance of Hamedan traffic control system. According to this analysis, the congestion index and traffic peak hours were determined. Also the relationship between vehicle speed and traffic congestion was predicted by neural network and the genetic algorithm. In this study areas of traffic were identified using Hamedan Traffic Control Center according with the speed of vehicles.