Case Study on an Adaptive Traffic Controlling Method Using Real-time Traffic Streaming

The traffic congestion in urban areas has emerged as one of the most notable issues in the past few decades due to the rapid rise of the number of vehicles in the country. The existing solutions available for countering this issue do not possess the capability to handle the excessive traffic congestion situations successfully. Presently, traffic light systems are widely used as a method to control the traffic flow in major junctions. However, the traffic lights operate according to a predefined set of light patterns and time cycles. The lack of ability in traffic lights to dynamically handle the traffic flows based on the real time traffic is an issue which should not be neglected. In this case study, an adaptive traffic controlling system for 4-way junctions, has been proposed where the aim is to minimize the traffic congestions that occur commonly in urban areas. The system was developed based on image processing techniques and fuzzy logic. Moreover, the image processing was used for recognizing the current traffic situation and the fuzzy logic based adaptive decision-making model was proposed for dynamically controlling the traffic. The test results of the system proved that the segmentation of image processing component had a sensitivity of 83.20% and a fall-out rate of 0.20%. The test results of adaptive decision-making component suggested that the adaptive system was better than existing system in 6 different phases out of the 12 tested phases. Thus, it can be concluded that the proposed system is capable of minimizing the traffic congestions considerably to meet the goals of reducing traffic related issues.