Identification and evaluation of the effective criteria for detection of congestion in a smart city

The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle-to-vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k-means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k-means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.