Traffic light controller

Project Report

Traffic Light Controller Using Fuzzy Logic

Group Members Mian Haseeb Gul Muhammad Uzair Adnan Inayat Mehran Khaliq Basharat

CU-959-14 CU-960-14 CU-648-13 CU-635-12 CU-049-14

Traffic Light Controller Using Fuzzy Logic Project Report

Abstract Traffic light plays an important role in human’s life, as in today’s modern world there are countless number of vehicles running on the road. In order to manage these large no of vehicles on roads it is necessary to improve the flow of traffic. In this Project, we have worked on traffic light controller using the fuzzy logic and image processing technique. For real-time image acquisition, the process is linked to the fuzzy logic controller which can give us a unique output for each input pattern given. We have used fuzzy logic tool box in Matlab, where the crisp output is sent to the microcontroller i.e. ARDUINO UNO(IC ATmega328). Introduction The main aim of the traffic light controller is controlling and management of traffic through the use of traffic signals i.e. ensuring the traffic safety at intersections and minimizing the delays. Traffic flow leads to various economic and environmental benefits. The traffic signal requires a certain cycle for its operation i.e. the output is yielded in Green, Yellow and Red colors. Figure (1). With the passage of time as technology is getting improved there are various technological advancements in traffic control being made.

Figure (1): Conventional traffic light

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Methodology We have selected two routes for our traffic light controller and named them as Route A & Route B. The vehicles are supposed to have a run on these routes.Our traffic light has been installed on the Tjunction of these two routes as shown in Figure (2), which is responsible for routing the cars according to Fuzzy Rules.

Figure 2:T-junction Our traffic light controller will get the input number of cars at T-junction through a vision camera using image processing tool box in Matlab. The main concept and idea can easily be understood with the help of a Block diagram which is explained and shown in Figure (3).

Figure 3: Traffic Light Controller using Fuzzy logic block diagram

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If the number of cars in Route A are greater than or equal to the number of cars at Route B then the Traffic Light Controller will get Green for Route A for a specified period, to let the cars passed, meanwhile for Route B the signal will be Red. Once all the cars at Route A are passed then the controller will get Green for Route B for a specific period while this time Route A will get a Red signal from controller. While at the execution of number of cars being counted by Microcontroller a delay logic will come into act. (Delay is defined as “summation of time specified for Route A and B) meanwhile it will start a countdown for the two Routes. While the countdown is in progress no further input is taken. As the Delay reaches to 0 it will take another input. Fuzzy Controller Fuzzification The Fuzzification comprises the process of transforming crisp values into grades of membership for linguistic terms of fuzzy sets OR Modifies inputs so that rule base can be interpreted and compared in rule base. The membership function is used to associate a grade to each linguistic term such as NEGATIVE LARGE (NL), NEGATIVE MEDIUM (NM), ZERO, POSITIVE MEDIUM (PM), POSITIVE LARGE (PL)

Figure 4:Input membership function route B in FIS 3


Figure 5:Input membership function route A in FIS

Fuzzy Rules The defined Rules for our Traffic Light Controller are given as under.

Table 1:Fuzzy Rules

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Figure 5:Rule Editor in FIS Defuzzification: Converts the conclusion to crisp input for the controller. In Mamdani FIS, expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzification(e.g centroid method). We have define output membership functions labels i.e. DECREASE, NORMAL & INCREASE.

Figure 6 :Output membership for route A in FIS 5


Figure 7 :Output membership for route A in FIS Lets us consider we have 3 cars at route A and 17 cars at route B then time for Green signal A after defuzzification will be 16 sec(Decrease) and time for Green signal B after defuzzification be will be 80 sec(Increase) as Shown in Figure 8

Figure 8: Rule Viewer Editor in FIS

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Simulink:

Figure 9:Simulink Fuzzy Crisp output is given to Controller (chart (Matlab)). If Signal A is Greater than Or Equal to Signal B the output of controller ‘y’ will be equal to ‘1’ it means that the rule will be triggered for Green time A first and then for Green time B and output ‘u’ of controller will be ‘zero’.

Figure 10: Signal A is Greater than Or Equal to Signal B Similarly when Signal A is less than Signal B output of controller ‘y’ will be equal to ‘zero’ and output ‘u’ of controller will be ‘1’ which means that the rule will be triggered for Green time B first and then for Green time A.

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Figure 11: Signal A is less than Signal B Hardware Code for Fuzzy Microcontroller Arduino FIST: MATLAB Fuzzy Inference System to Arduino C Convertion Source=http://www.makeproto.com/projects/fuzzy/matlab_arduino_FIST/index.php

Figure 12:C code for Fuzzy microcontroller 8


Microcontroller for Signals control

Figure 12:C code for Signal Control

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Traffic light controller

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