Repositioning Fleet Vehicles: A Learning Pipeline.

Managing a fleet of vehicles under uncertainty requires careful planning and adaptability. We consider a ride-hailing problem where the operator manages vehicle repositioning to maximize responsiveness. This paper introduces a supervised learning pipeline that uses past trip data to reposition vehicles while adapting to fleet activity, a geographical zone, and seasonal or daily request variation. The pipeline incorporates trip features, such as medical motives of transportation for ambulances and the time and location of the trips. This provides a better estimate of the probability that a given vehicle will be required in a particular sector and provides insights into which events and trip features should be incorporated into the decision-making process for better fleet management and improved reactivity. This tool has been developed for, and used by, operators of an ambulance company in Belgium. Using predictors for ambulance repositioning reduces at least 10% of the overall fleet reaction distance.