An Efficient Multilevel Framework for Prediction of Optimized Ambulance Routes Using Random Forest Classifier - S. Nagamani; V. Bhuvaneswari (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Ambulance Routing has been a classical research problem for years because of its multiple constraints and multiple objectives. While the primary objective is to save human Life, finding optimistic routes for Ambulances to transfer the patient to the nearest hospital by travelling in traffic congested routes during peak and non-peak hours with quick response time and minimized total cycle time remains a major challenge.While several techniques, Mathematical models and algorithms are used to arrive at a solution, data mining and machine learning techniques provides efficient methods in achieving the optimal results for the Ambulance Routing Problem. In this paper, Multilevel framework is developed for predicting the Optimized Ambulance routes using Random Forest Classification Trees. First, by using Advanced A* Algorithm the routes are determined for the Ambulances during peak and non-peak hours of traffic. The routes are calculated using the minimum dispersion index as a heuristics along with other constraints such as speed of the Vehicle, distance, number of vehicles crossing each junctions to assess the traffic conditions. Random Forest Classifier is used on the spatiotemporal data sets such as time of starting of Ambulance and the alternate route taken to predict the optimized routes which helps in further improving the response time and total cycle time. The criticial spatiotemporal features required to predict the Optimized Ambulance Routes are very well brought out by using Random Forest Trees.The experimental results reveals that the predicted routes improves the response time by 30 % and total cycle time by 40 %.