Abstract:

Abstract Congestion in huge cities around the world has recently become one of their biggest issues. The rapid growth of automobiles and the lack of appropriate roads to handle a big number of vehicles are the causes of the traffic jams. Various existing methods like Conventional or static time-based traffic management system provides drawbacks such as low edge detection accuracy, image blurring, etc. In this paper to improve these drawbacks Capacity map Generator (CmapGen) algorithm is proposed. The proposed CmapGen algorithm uses image processing methods to determine the current area-wide traffic density at the intersection of traffic lights. The traffic density calculated for the live roads using the CmapGen method will be used to identify the available traffic light durations. The vehicle density will be determined using the frames produced from the traffic video files. The outcome demonstrates that by processing the edge detection technique results through several image processing techniques like thresholding, blur, etc., the suggested CmapGen algorithm increases the edge detection technique's accuracy. The proposed method is contrasted with other approaches, including Canny techniques and conventional or static time-based traffic management systems. The average traffic density for Canny is 23.91, whereas it is 31.12 for CmapGen. The suggested CmapGen has a higher average traffic density as compared to the traditional Canny approaches. The proposed technique yields superior results compared to the canny edge identification method. Therefore, it is more beneficial in a smart traffic control system to determine when to change traffic lights by computing the area-based traffic density in real time.

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