Advances in Fuzzy Systems

Volume 2015, Article ID 378156, 11 pages

http://dx.doi.org/10.1155/2015/378156

## A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

^{1}Department of Computer and Information System, Bethlehem University, Bethlehem, State of Palestine^{2}Department of Computer Architecture and Technology, University of Granada, Granada, Spain^{3}Department of Computing, Faculty of Sciences, University of Salamanca, Salamanca, Spain

Received 17 May 2015; Revised 21 July 2015; Accepted 2 August 2015

Academic Editor: Ning Xiong

Copyright © 2015 S. M. Odeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

#### 1. Introduction

Since an intelligent traffic signal system is a major part of an intelligent transportation system, it is a challenge to increase the work efficiency of these traffic signals in order to reduce traffic jams and congestions as well as vehicle emissions in the metropolitan area. Additionally, other objectives include improving the traffic safety at the intersections and reducing the amount of trip time and the time that vehicles spend idling, which decreases the fuel consumption. Thus, this will cause a decrease in the amount of CO_{2} emissions. The number of vehicles has increased considerably in the last few years. The National Automobile Dealers Association in USA published data in 2007 with respect to the total number of vehicles, since this study; that is, the Federal Highway Administration reported that the number of motor vehicles has grown approximately eleven million on American roads [1]. According to the Bureau of Transportation Statistics for 2009, there are more than 254 million of registered passenger vehicles [1]. Moreover, according to the data released by the transport department in Mumbai between 2006-07 and 2013-14 the total number of vehicles has increased by more than a half in the last seven years [2]. Since the number of vehicles has increased while the road length has remained the same, therefore, the traffic signals are not capable of solving the problem of congestion by using the same efficiency. Therefore, there is a need to develop intelligent traffic signals that overcome the congestion and traffic jam. Traffic congestion is also causing higher noise and pollutant levels that are becoming a major burden for people and the environment. Thus, we should be able to make traffic signals interact with congestion and thus help people and goods reach their destinations quickly and safely. This means saving time, effort, and, of course, money.

Many solutions were proposed and applied to solve this problem. These solutions depend on different controller programming systems, such as Fuzzy Logic Controller and neural network. These systems were applied on an isolated intersection and on two intersections of two roads; there were some enhancements on the performance of the traffic signals. The Vehicle Actuation (VA) System has been used to solve this problem. This system adjusts the green time according to the vehicles demand on all intersections. Although this system is more responsive than fixed time, it can be still ineffective if there are long queues of vehicles on conflicting junctions.

The intelligent traffic signal system proposed in this paper depends on the hybrid combination of fuzzy logic (FL) [3] and Genetic Algorithms (GAs) [4]. The flexible and robust nature of the developed fuzzy controller allows it to model functions of arbitrary complexity while at the same time being inherently highly tolerant to imprecise data [5]. On the other hand, the maximizing capabilities of GAs enable the fuzzy design parameters to be optimized in order to achieve an optimal performance [6].

The system proposed in this paper is that the number of vehicles on the road is counted by a video image detection object system that was used and discussed in [7]. Then we will apply those outputs as inputs to our system, composed of FLCs with a set of fuzzy rules [8, 9]. The knowledge base was used in the system as the population of the GA, meaning that a single rule, containing the description of the corresponding fuzzy set, is an individual of the population. The other application of the system is the detection of the abnormal situations like incident detection and the level of congestion. Our system applies Fuzzy Logic Controllers together with Genetic Algorithm (FLCGA) to four intersections with four directions controlled by traffic signal controllers. Those works applied their system to just two intersections. The results of our system show an improvement in the performance using the FLCGA rather than traditional Fuzzy Logic Controller, FLC, and also rather than Conventional Traffic Signal Controller, CTC.

#### 2. State of the Art

Recently, many researchers have focused on other dynamic control signals that adjust the timing and phasing of lights according to limits that are set in controller programming. References [10–12] used Fuzzy Logic Controllers for an isolated intersection in their controlling programs. References [8, 13] used GA to obtain the fuzzy control parameters in their systems, [9, 14] used neural networks to improve the fuzzy control results. The work [15] presents a simulator built for a two-way traffic junction where each way has a single lane of traffic flow. The results of the proposed fuzzy controller exhibited successful performance at constant traffic volumes; in our system we built the simulator for two roads with three lanes for each in three directions: right, left, and straight. The objective of [12] was to implement a fuzzy signal duration control based on hardware, but it was not possible because of the memory limitation of the program, as the authors reported. Paper [16] discussed the implementation of an intelligent traffic signals’ control system using fuzzy logic technology that can mimic human intelligence for controlling traffic signals. We implement FLC technology with GA to manage the congestion on the four intersections in a more effective and smooth way. References [17–19] worked on traffic signal control for an isolated intersection signal (adjacent intersection) with fuzzy controller methods. References [8, 13] update fuzzy control rules by means of GAs. On the other side, some other researchers used fuzzy logic for controlling traffic in multiple intersections. Some work applied a FLC to adjust the cycle time, phase split, and offset parameters on only two-way streets, which were evaluated without considering any turnings [20]. Reference [21] used fuzzy reasoning to control vehicle moving on two adjacent intersections; meanwhile, [22] used a simple two-phase fuzzy signal controller. They compare their fuzzy method to minimize delay time of adaptive signal control with optimal cycle time. Their results were satisfactory, better than the adaptive method used for comparison. We used FLC with GA for multi-intersections and we got better results from using FLC alone and Conventional Traffic Controller.

They found that FLCs lead to shorter vehicle delays and a lower number of idle vehicles. Thus, the length of the current green phase is extended or shortened depending on “*Arrival*,” which is the number of vehicles approaching the green time interval, and “*Queue*,” which corresponds to the number of queuing vehicles in red or green phases [11]. The system proposed in this paper will adjust the timing and phasing of the green lights according to the current situation in four intersections; every intersection will be controlled by traffic signals that will apply a hybrid algorithm. Thus, FLCs will decide how much the green light interval time shall provide at an intersection. They will be optimised by means of a GA, improving the best decision made by the FLCs in order to obtain a higher performance. This performance can be measured considering the reduction in the waiting time and the total amount of vehicles that arrived to the* Queue* of the four intersections. This proposed system is known as FLCGA.

#### 3. Preliminary Concepts

##### 3.1. Genetic Algorithms

The basic principles of GAs were first proposed by Holland in 1973 and inspired by the mechanism of natural selection [23]. Genetic Algorithms (GAs) are optimizer and it is a stochastic beam search which begins with a set of random generated finite strings called individual and the set of individuals called population. The production of next generation is done by selection of best individuals that were rated by the evaluation function called fitness function [4]. These parameters are regarded as the genes of a chromosome and can be structured by a string of values in binary form [24]. Fitness function is a function that calculates a value that is used to reflect the degree of “goodness” of the chromosome for the problem which would be highly related to its objective value. In general and in most of the application, GA starts with a randomly generated population of n chromosomes (candidate solutions to a problem). Calculate the fitness of each chromosome in the population. The following steps are the main fundamental of GA [25]:(a)Select a pair of parent chromosomes from the current population, with the probability of selection being an increasing function of fitness.(b)With probability (crossover rate), perform crossover to the pair at a randomly chosen point to form two offspring.(c)Mutate the two offspring at each locus with probability (mutation rate), and place the resulting chromosomes in the new population.(d)Replace the current population with the new population.There are a wide range of application fields that GA can be used on: State Assignment Problem such as Traveling Salesman Problem, TSP, and economics where it is applied in game theory; also, it is widely used in computer aid design.

##### 3.2. Fuzzy Logic Controller

Fuzzy logic was introduced by Zadeh in 1968 and is based on mathematical representation of human knowledge and experiences [26]. Fuzzy Logic Controllers can be considered as knowledge-based systems, incorporating human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions [3]. Fuzzy logic allows the manipulation of linguistic data (Large, Medium, and Small) and inaccurate data, as a useful tool in the design of signal timing [14].

Fuzzy Logic Controllers have been successfully implemented in many systems that have inherent uncertainties. These systems include, among others, antilock brakes system (ABS) and camera-focusing system, where traditional modelling techniques and controllers do not usually provide satisfactory system performance, in addition to many earlier applications of fuzzy logic to traffic signal control. Reference [27] established a two-stage method for intersection signal timing control based on neurofuzzy network. Reference [17] provided a fuzzy logic signal controller for a four-way isolated intersection. This model is suited for mixed traffic, including the high proportion of motorcycles. Reference [28] established a fuzzy control model for traffic light with countdown ability. It implemented a self-adapted fuzzy controller for intersection signal control based on the conception of quantitative flow to fuzzy traffic flow. Reference [29] presented a traffic signal control method based on fuzzy logic for an isolated signalized intersection. The current green signal can be extended or terminated in response to changing traffic conditions [30].

Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The process of fuzzy inference involves membership functions, fuzzy logic operators, and if-then rules. There are two types of fuzzy inference systems [31], Mamdani’s and Sugeno-type systems fuzzy inference method. In this paper, we will use Mamdani’s fuzzy rules, because the output of Sugeno type must be constant or linear, while the Mamdani type expects the output membership functions to be fuzzy sets.

#### 4. Algorithm Architecture and Modelling

The algorithm of our proposed system takes the input data of the number of vehicles on each lane of the road in each intersection from a video object detection system. This system captures the image from the video stream. Then it performs a video image processing to detect the objects in the image that represent the vehicles in the road; then the system returns the number of these vehicles (objects). The work [7] describes this system in more detail. The treatment of this data is done by fuzzy logic that was characterized by a set of rules that defined antecedent. These rules include all possible scenarios for each traffic jam in every intersection. Then GA is applied to optimize the best performance of FLC as shown in Figure 1. Vehicle detection and counting is important in computing traffic congestion and this represents the inputs to our system. The number of vehicles in each lane and side of the intersection is counted while the traffic signal is red.