Optimal fuzzy traffic signal controller for an isolated intersection

Abstract

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Fuzzy logic system (FLS) is the method applied to develop the intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The FLS controller (FLC) uses genetic algorithm to tune its parameters during learning phase. Finally, The performance of the intelligent FLC is compared with the performance of a FLC with predefined parameters and three simple fixed-time controller. Simulation results indicate that intelligent FLC significantly reduces the total delay in the network compared to the fixed-time method and FLC with manual parameter setting.

ANFIS Traffic Signal Controller for an Isolated Intersection

“…An GA-FLC (Araghi et al, 2014) is also implemented as a benchmark. Similar membership functions are designed for GA-FLC Fig.…”

“…In all controllers, the appropriate green time for each phase is generated based on the queue length in four links. ANFIS controller uses the rule base obtained during training and GA-FLC and fixed-FLC apply the same rule base that presented in our previous work (Araghi et al, 2014). Rule base is presented in table 1.…”

“…Based on the research is done in (Araghi et al, 2014), parameters of GA-FLC are optimally tunned using genetic algorithm with the purpose of minimizing delay times. The genetic algorithm used has 30 populations for optimizing seven parameters of eleven output membership functions.…”

“…6 shows the parameters of output membership functions after train- Table 1: FLC Rule Base. In this table S stands for small, M for medium, L for large, and ∼ is for negation (eg., ∼S means not-small, which could be either medium or large) (Araghi et al, 2014). Figure 6: Membership functions of the GA-FLC output after optimizing the parameters (Araghi et al, 2014).…”

“…Before start of each phase the appropriate green times for that phase is estimated and sent to the controller. We evaluate the performance of the ANFIS controller with one of our previous studies GA-FLC (Araghi et al, 2014) and a FLC uses predefined parameters in its fuzzy sets named fixed-FLC. In addition, a fixed-time controller with three different values for its green phase time is included in performance evaluation tests.…”