On Recommending Urban Hotspots to Find Our Next Passenger

The rising fuel costs is disallowing random cruising strategies for passenger finding. Hereby, a recommendation model to suggest the most passengerprofitable urban area/stand is presented. This framework is able to combine the 1) underlying historical patterns on passenger demand and the 2) current network status to decide which is the best zone to head to in each moment. The major contribution of this work is on how to combine well-known methods for learning from data streams (such as the historical GPS traces) as an approach to solve this particular problem. The results were promising: 395.361/506.873 of the services dispatched were correctly predicted. The experiments also highlighted that a fleet equipped with such framework surpassed a fleet that is not: they experienced an average waiting time to pick-up a passenger 5% lower than its competitor.