Traffic control is becoming an increasing issue in the United States every day, more and more wrecks are occurring due to traffic jams and traffic stops. This is a major issue that needs to be given more careful attention to, and until recently, it was just overlooked. Researchers are looking for more economically and efficient ways to control traffic, as in new and improved software for stoplights and locations for these new methods. The government has also invested time and money into this research and into furnishing these new systems and installing them in locations around the United States.
Not only are government institutions providing research for the issue at hand, but universities, as well as institutions in other foreign countries; a major one being that of Japan. “The conventional fixed-time traffic control system is one of the most popular and oldest in the world” (Pranevicius). It reiterates the fixed signal timings originated from historical traffic patterns. The pre-timed control does not adapt to the different traffic conditions in real time. Another previously used model is the optimum cycle time procedure, used for fixed signal plans based on expected demands. It is not a very effective system for heavy trafficked intersections or rush hours. This is why adaptive methods have been sought, to regulate with real-time traffic data. Development of computer technology has allowed these real-time adaptive methods to be utilized in the most efficient way possible. One of the models created and practiced by the Dept. of Business Informatics, Kaunas University of Technology in Lithuania, is the Knowledge Based Traffic Signal Control Model for Signalized Intersections. Their proposed method is based on the “Fuzzy set logic”, which is an extension of the classical set. In researching this model, it described the algorithm they used to adapt the Fuzzy set and improve it based on their standards. The propositioned method functions as if it were a police officer using his expertise to control the flow of traffic through the stopping location. By doing so, they have used two parameters as an input; one being that of the average queue length at green, which is the number of vehicles that did not pass the intersection during the green phase. Two would be that of the average queue length at the next green. Essentially, established on the traffic conditions, the system dictates whether to delay the current green signal or to conclude it. In other words, fixing the problem of the traffic lights is based on mathematical theories and algorithms, which is where the scientific part appears into perspective. Another form of systems used is the expert system, which is an applied computer program that uses experimental approaches developed to solve certain classes of troubles. A specialist creates expert systems with knowledge in a problem area and an engineer who codes this information into a format that the computer can utilize to solve the problem. The fuzzy set method that I mentioned earlier is based on this expert system knowledge, to control traffic at single intersections. To gain the results of the proposed algorithm, the performance is compared against the performance of a fixed-time signal operation, and against the fuzzy logic control. For the fuzzy logic controller it shows; the average number of vehicles in the lanes of the present green phase, arrival speed of the present green phase, and the average number of vehicles in the lanes of the next green phase. They then divide the number of the current green phase and the “next” green phase into four different sets: “short”, “medium”, “long”, and “very long”. The arrival rate has three sets: “low”, “medium”, and “high”. Forty-eight different rules are used in the judging process of the fuzzy set, to determine whether or not it was successful in “real-time” traffic control. Pre-timed signal control is the system used to compare against the fuzzy logic