iTaxi: Context-Aware Taxi Demand Hotspots Prediction Using Ontology and Data Mining Approaches

It has been estimated that over 60 thousand licensed taxis in the Great Taipei area are not occupied over 70 percent of driving time on average. However, the taxi company, TaiwanTaxi, indicates that even in rush hour, there are customers whose requests are not satisfied. The demand and supply are not paired, causing not only customers wait too long for a cab, but also taxi drivers waste time and fuel to wander around the streets. In this paper, it uses spatial statistics analysis, data mining and clustering algorithm on historical data of taxi requests to discover the demand distribution, which varies from different environment contextual information such as the location, time, and weather. Finally, the predicting system then predicts potential hotspots of taxi requests and provides hotspots information for drivers to reduce vacant time of the taxi.