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