Lifesaver: A Vade-Based Intelligent Ambulance Positioning System for Optimal Emergency Response and Alert System

Every day, the number of traffic accidents rises as the automobile population increases.According to a survey by the World Health Organization (WHO), 1.3 million people die and 50 million are wounded annually around the globe.Most people die because they don't get medical help at the scene of an accident or because it takes too long for rescuers to get there.The time after an accident can be optimally used to make a difference between a life saved and life lost, if recovery actions are able to take place in time.However, routing problems and traffic congestion is one of the major factors hampering speedy assistance.By identifying sites where the possibility of accidents is higher and the closest spot for ambulance placement, the response time can be greatly reduced.In order to operate efficiently as well as effectively ambulances should be deployed in areas where there is maximum demand and the ambulance should be able to reach the victim within a drive time of five minutes.This project suggests a specific way to shorten the time it takes for an ambulance to arrive at the scene of a road accident.To achieve this, the project aims to revolutionize emergency response strategies by proposing a novel unsupervised generative clustering approach employing Variational Deep Embedding (VaDE).Additionally, this proposed system includes real-time alerts to both hospitals and traffic departments, facilitating route clearance for expedited ambulance travel.Unlike traditional clustering methods, Variational Deep Embedding (VaDE) is a 4-step data generation process that uses deep neural networks and a Gaussian Mixture Model to optimize ambulance positioning strategies.By having an ambulance on site or in close proximity to the spots venue, the response time can be significantly reduced and thereby save precious lives.