Paper Details

Use of automobiles is increasing throughout the world, particularly in large urban areas. There has been theproblem of traffic congestion with the increase in the number of vehicles in cities. Thus, there is a requirement for smarttraffic control methods for better accommodating the increasing demand. Therefore, the transportation system willcontinue to grow, and intelligent traffic controls have to be developed to face the road traffic problems. In this paper acomparison has been drawn for Neuro-Fuzzy (NF) based smart traffic control system and fuzzy logic based smart trafficcontrol system. An adaptive neuro-fuzzy inference system is developed and tested against various traffic situations. Herewe have trained the Adaptive Neuro-Fuzzy Inference System (ANFIS) system by various traffic situations so that ANFIScan draw the membership functions and corresponding rules by its own. Inputs which are generally used are Gap betweentwo vehicles, last time vehicles that haven’t passed during last green phase, delay at intersections, vehicle density, arrivalrate, leaving rate and queue length. Arrival rate of the particular phase and last time vehicles that haven’t passed duringlast green phase are taken as inputs to the system by the considering the practical applicability. Output of both the systemare compared in terms of the average waiting time of the vehicles. ANFIS based traffic control system has been foundmore efficient as the average delay of the vehicle and the waiting time of the vehicles at the int ersection have beenreduced

Suraj Seesara, Jagrut Gadit, “Smart Traffic Control Using Adaptive Neuro-Fuzzy Inference System(ANFIS)”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 5, pp. 295-302, May 2015.