Reduction of Ambulance Response Time and Accident Detection using IP and CPM

Abstract: The usage of vehicles is rapidly increasing due to recent technological and economic development, and at the same time, the lack of infrastructure against the demand is leading to an increasing number of accidents and fatality of life. The trivial issues in our life system motivated us to come up with an application to automate this process and save lives. With a review of literature and brainstorming, I proposed the project on a smart traffic management system using image processing. The objective of this project is to develop a detect ambulance using image processing and machine learning techniques. In the first phase, we determined traffic density to minimize the delay caused by traffic congestion and to provide the smooth flow of vehicles. The density of vehicles on each side can be identified by using datasets. If the density is low on a particular side, the period for that side is normal and if the density is high the period will automatically increase compared to normal density. In the second phase, we simulated a crash or accident detection and for the prototype consideration, we have used static accidental image and trained model. In the third phase, analyzed ambulance detection using the dataset, for the prototype consideration for this, used static ambulance image and trained dataset. On detection of an ambulance, the traffic light is automatically changed to green. In each phase, the data updating and monitoring are provided. This scheme is fully automated and identifies the emergency vehicle and controls the traffic lights dynamically. Hence, the traffic management module is done using image processing techniques.