It is imperative for ambulance services to minimize response time to emergencies while managing operational costs. Application of machine learning can help ambulance services to optimise their operationality, but for this to be practical GPS based fleet tracking must be adopted en mass.
Demand predictions are crucial to operations decisions such as staff / fleet management, placement of base locations,and dynamic deployment. Predicting ambulance demand is critical for staff / fleet management and dynamic deployment. There are several challenges though.
Dataset for such calculations is large but demand for ambulances in the locality at time of study might be low. The complicated geography of modern Indian cities – especially like Delhi and Hyderabad, that see large-scale unplanned development alongside pre-existing historical settlements – makes ambulance operation a very difficult task. An accurate study of ambulance demand and operation becomes more difficult in such situations.