ANFIS Traffic Signal Controller for an Isolated Intersection

Abstract

Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

“…Similarly, in [19], critical points on a road in rural areas are identified on the basis of collected data on traffic accidents. Traffic control at intersections with traffic lights can also be carried out using ANFIS models [20,21]. This involves reading out external data on the current state of the intersection, and forwarding them to the model that processes them and reacts in accordance with the learned rules [22].…”

An Adaptive Method for an Isolated Intersection Under Mixed Traffic Conditions in Hanoi Based on ANFIS Using VISSIM-MATLAB

“…The average waiting time, queue length, and delay time of their system were the lowest as compared to the traditional and fuzzy systems. Araghi et al [23] used ANFIS for optimizing green times and minimizing travel delay. The performance of their method is better when compared with three other methods, including the fuzzy logic-based genetic algorithm, the fixed FLS, and the fixed-time control system.…”