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The decision to rollout a vehicle is critical to fleet management companiesas wrong decisions can lead to additional cost of maintenance and failuresduring journey. With the availability of large amount of data and advancementof machine learning techniques, the rollout decisions of a supervisor can beeffectively automated and the mistakes in decisions made by the supervisorlearnt. In this paper, we propose a novel learning algorithm SUPAID which undera natural 'one-way efficiency' assumption on the supervisor, uses a rule miningapproach to rank the vehicles based on their roll-out feasibility thus helpingprevent the supervisor from makingerroneous decisions. Our experimental resultson real data from a public transit agency from a city in U.S show that theproposed method SUPAID can result in significant cost savings.

SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems