Application of fuzzy logic to control traffic signals



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Keywords: 
Fuzzy logic, Traffic control, Triangular membership function, Fuzzy rule base system. 
INTRODUCTION 
Traffic congestion is one of the critical problems to be resolved to improve the economy of any country. The proper 
way of controlling traffic congestion is done by using traffic signals. Due to increase of vehicles in roads, public 
behavior and fixed time controlled traffic signal systems have not provided a solution to high traffic congestion. The 
aim of the traffic congestion problem is to minimize the delays in roads by effectively using the existing traffic 
signal systems without constructing new roads. Traffic system is more dependent on parameters such as time, day, 
season, weather and unpredictable situations. The difficulty and uncertainties present in the existing traffic system 
can be rectified by using an intelligent traffic control system which continuously sense and adjust the timings of 
traffic lights depending on the traffic jam. If these parameters are not taken into consideration, the traffic control 
system will create delays. The difficulty and uncertainty of traffic clogging made an ultimate representation of such 
research. Traffic congestion patterns expand over time period as an effect of communications between target and 
congestion over space. At its most basic congestion is caused when the level of traffic exceeds road capacity. L.A 
Zadeh proposed a fuzzy set theory to deal with uncertainties present in real world situations. The fuzzy logic 
controlled traffic light utilizes sensors that tally cars and permits an improved estimation of modifying traffic 
patterns .To proposed the average weight of vehicles at a intersection lane an adaptive controlled design strategy 
was proposed by Asthuosh Choudhary [1]. A comparison between different fuzzy logic control algorithms was 
made by I N Askerzade [2]. Babangida Zachariah [3] introduced a fuzzy logic inference system to optimize state 
phase scheduling of traffic light system (SPSTLS). Using states machine B Dilip [4] proposed an efficient design. 
Using sugeno method Erwan Eko Prasetiy [5] proposed an adaptive traffic light controller. A traffic signal control 
method for a 6-phase intersection was proposed by Fuyang chan [6]. Ms .Girija H Kulkarni [7] using VB6 
environment in MATLAB proposed a fuzzy traffic controller for an isolated intersection. This simulation result 
verify the performance of our proposed integrated traffic light control system using RFID technology and fuzzy 
logic was projected by Javed Alam [8]. Jarko niittymaki [9] make suggestion fuzzy control principles are very 
competitive isolated multi-phase movements. Kamlesh Kumar Pandey [10] proposed a fuzzy decision support 
system and component of fuzzy controller with fuzzy rule base. Navneett Kaushal [11] proposed two algorithms of 
fuzzy rule base system for an isolated traffic intersection in VB6 environment. O. M Olanrewaju [12] had proved 
that the pedestrian delay has significant contribution to traffic control to enhance the safety of the pedestrian. 
Constructed the effectiveness and actual situation of the traffic control process are investigated by Sandeep Mehan 
[13]. In this paper controlling traffic flow for an isolated four lane traffic junction using fuzzy logic technique are 
discussed. This paper has been organized as follows. In the following section II a outline about fuzzy traffic signal 
controller is described briefly . Then assumptions and constraints of isolated four lane traffic controller is introduced 
in section III are explained. Then in the next section IV and V about fuzzy inputs, output, linguistic variables and 
The 11th National Conference on Mathematical Techniques and Applications
AIP Conf. Proc. 2112, 020045-1–020045-9; https://doi.org/10.1063/1.5112230
Published by AIP Publishing. 978-0-7354-1844-8/$30.00
020045-1


their membership functions. Then different fuzzy rule bases have been set for traffic signal processing and a 
simulation of fuzzy rules using mat lab is implemented and the results are presented 

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