Quantification of Traffic Congestion Based on Vehicular Speed Under Heterogeneous Flow Conditions Using Fuzzy Inference Model

On transport networks, traffic congestion is a phenomenon that arises as usage increases and is marked by slower speeds, longer travel times and increased queuing of automobiles. In plain terms, “the ability of a vehicle to move forward in a traffic state” defines congestion. Traffic congestion in smart cities has been a major concern. In heterogeneous flow like India, congestion impacts the movement of people both in perception and in reality that leads to consumption of time, energy and also leads to the pollution. In order to save precious human life, eliminate road accidents and the essence factor called time, it is essential to ensure a proper measure for traffic congestion. Earlier there were several attempts made to develop different approaches for congestion analysis. At present the congestion levels of ten different road stretches of Visakhapatnam city within the central business district (CBD) area. The key purpose of this analysis is to introduce a scalable fuzzy logic traffic flow model capable of optimally forecasting traffic to define the city's congestion levels by taking into account parameters such as vehicle volume, average speed and road speed limits by using MATLAB and to generate the desired congestion index of the specified study stretches. This research sets the framework for the forecast, early warning and constructive alleviation of traffic congestion