DeepRoute: Smart Traffic Optimization with AI-Powered Insights

Abstract

Traffic congestion, which has become rampant in recent times, has fused itself in every nook and corner of cities. The cities appear to be heavily impacted by it further. Road Traffic density should be known in real-time to ease signal control and facilitate efficient traffic management due to its bigger nature. Some causes of Traffic Jams include: Insufficient capacity, excessive demand, delays in red light, etc. However, insufficient capacity and infinite demand have some connection, but there are hard-coded delays for each light and traffic independent. Henceforth, traffic management must improve and optimize the simulation to build it in an advanced way that satisfies the increasing demand for traffic management measures. Traffic Management Imaging based processing and monitoring systems have become visible for real-time information, lamp measures, and updates to travelers recently. Image processing can also measure traffic density. The live images acquisition procedure from the camera will be described in this project. For real-time traffic density with image processing, cameras are placed in traffic migration. It also incorporates traffic-light algorithms, which switch to a particular road according to the density of vehicles on it. It is devised to minimize traffic congestion and reduce the number of road accidents. This allows harmless passage to human beings, reduces fuel consumption, and cuts time spent waiting. An extensive data base is also available for future planning and evaluation of roads. Over much more advanced stages, three or more signals in traffic can then confer with one another to relieve traffic and blockage in traffic. No other devices are used mounted on the road - actually processed by the system itself - and only photographed vehicles recognize them before counting. Cameras near an intersection will be the area to produce an image series. Changes in traffic light conditions could also be controlled on the basis of image processing easily. Thus, under such conditions, green light on empty roads means that time wasted can be reduced and traffic jams can be reduced. The use of real photos of the traffic condition further enhances reliability in estimating vehicle presence. Instead, it is very likely much better than system deployment based on detecting the metallic content of vehicles