A Case Study for Traffic Control Signal at Four Way Intersection Road

The research article “A Case Study for Traffic Control Signal at Four-Way Intersection Road” was written by four people, Kamlesh Kumar Pandey, Rajat Kumar Yadu, Pradeep Kumar Shukla and Narendra Pradhan. The authors of the article have focused on coming up with a solution which has the capability of overcoming one of the major problems in the world, the traffic management. They have pointed out the important fact regarding the difficulty that a single person faces when handling traffic congestion in a multi-lane intersection. Throughout the article, their goal is to describe a mechanism to handle large traffic flows in four way junctions using fuzzy logic. According to the authors, fuzzy logic technology possesses the capability of mimicking the human intelligence for controlling traffic lights.

They expect to use two different types of traffic controlling methods for the intersection where the first method is to use a preset cycle time to change the light patterns. The other type is to combine the preset cycle time with a sensor in order to dynamically change the light patterns based on fuzzy logic. The structural diagram of their system is given below.

Figure 01 - Structure of the System

They use two sensors to gather information of the vehicles where one of them is responsible for counting the number of vehicles passing the traffic lights while the other sensor counts the number of vehicles coming towards the intersection. The fuzzy logic controller too functions as an important part of the system as it controls the length of the green light time according to the traffic condition.

Figure 02 - Structure of the Fuzzy Controller

The fuzzy logic controller determines whether to extend or terminated the currently implemented light pattern (i.e green light time). The degree of priority that should be allocated to each lane is analyzed by the set of fuzzy rules and relevant decisions are made based accordingly. The fuzzy controller design is said to contain several functions that include Fuzzification, Fuzzy Decision Making, Defuzzification and Extension Time.

Fuzzification

According to the authors, the process of fuzzification involves converting crisp set data into a fuzzy set. The number of vehicles counted by the sensors, is sent to the system that facilitates fuzzification. The proposed fuzzy set supports a numerical number range between 0 and 1.  

Fuzzy Decision Making 

The fuzzy decision making is a process that takes the values from the previously converted data to make decisions using the knowledge based and the fuzzy rule base which are integrated within the decision making system. The knowledge base functions as subsystem to determine the best possible pattern that can be implemented using the input variables. The knowledge base is said to be constructed using the results obtained by traffic simulation. Further, the functions of knowledge base are complemented by rules and searching algorithms such as heuristics and hill clamming search.

Defuzzification

Defuzzification is the process that converts fuzzy output values back to the crisp values. The output value of this process is related to how long the green light should be lit.

The authors have tried to come up with a really good solution through the use of fuzzy logic to overcome the traffic congestion problem. However, the research article contains a lot of ambiguous content since the language that is used throughout the article cannot be considered good enough for an article. 

Reference:

Kamlesh Kumar Pandey, Rajat Kumar Yadu, Pradeep Kumar Shukla3, Narendra Pradhan, "A Case Study for Traffic Control Signal at FourWay Intersection Road" International Journal of Computer Techniques -– Volume 2 Issue 4, July - Aug 2015, ISSN: 2394-2231