SMART TRAFFIC CONGESTION CONTROL
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SMART TRAFFIC CONGESTION CONTROL It is clear that transportation has a major impact on human life throughout history. All aspects of life either political, economic, etc. depend on transportation in one way or another. The main problems of the transportation system have been solved over time with the application of new inventions and using advanced technologies. Innovation continues now and concern is to increase the transportation efficiency of system facilities. Traffic congestion is one of the biggest problems facing cities around the world, and perhaps the most important factors that led to it are the large increase in the number of people, the lack of good infrastructure and poor coordination between traffic lights. Among the problems facing us in Palestine is the presence of the Israeli occupation, which works to hinder the development and prevent drilling in many areas of infrastructure development, in addition to the lack of advanced radio communication networks such as the fifth generation. Traffic management can play a role in reducing traffic congestion, improving safety, and reducing gases produced by cars. It also reduces human control of the vehicle. To solve this problem, we used several methods to detect and count cars. 1. Solve traffic congestion by image processing This method is used to track vehicles, counting, the average speed of each vehicle, analysis of traffic targets and vehicle classification. Parameters can provide us with complete information about traffic flow, which meets the requirements of traffic management theory, and image tracking for moving vehicles can provide us with a description Quantification of Traffic Flow . 2. A Computer Vision Based Vehicle Detection and Counting System in MATLAB The proposed method uses the background subtraction technique to find the forward compounds in the video sequence. In order to detect moving vehicles with high accuracy and explained. First, the background subtraction technique is used to find the image of the objects in the foreground. Second, the area of interest in the image is manipulated with several techniques, including adaptive morphological processes, to remove noise and improve foreground objects. Then, the center point is calculated for each object in the front and used to represent the vehicle's position. A dividing line separates the right from the left so that each portion is counted. Finally, vehicles are recorded and counted 3. Solve traffic congestion by Video processing in Python using video processing process some things can be controlled and the video analyzed motions. Video processing includes pre-filters, which can cause contrast changes and noise elimination along with video frames pixel size conversions. Highlighting particular areas of videos, deleting unsuitable lighting effects, eliminating camera motions and removing edge-artifacts are performable using video processing methods. OpenCV library of python is equipped with functions that allow us to manipulate videos and images. OpenCV Python makes use of Numpy, which is a library for numerical operations with a MATLAB-style syntax. Based on our project, we counting the cars and controlling the traffic light according to the number of cars, so that if the number exceeds the number 15, the green signal will open for a longer period, and the rest of the intersections will be red. But we were unable to apply this thing at a real crossroads due to the prevailing conditions in the country.