An effective taxi recommender system based on a spatiotemporal factor analysis model

The taxi fleet management system based on GPS has become an important tool for efficient taxi business. It can be used not only for the sake of fleet management, but also to provide useful information for taxi drivers to earn more profit by mining the historical GPS trajectories. In this paper, we propose a taxi recommender system for next cruising location which could be a value added module of the fleet management system. In the literature, three factors have been considered in different works to provide the similar objective, which are distance between the current location and the recommended location, waiting time for next passengers, and expected fare for the trip. In this paper, in addition to these factors, we consider one more factor based on drivers experience which is the most likely location to pick up passengers given the current passenger drop off location. A location-to-location graph model, referred to as OFF-ON model, is adopted to capture the relation between the passenger get-off location and the next passenger get-on location. We also adopted a ON-OFF model to estimate the expected fare for a trip started at a recommended location. A real world dataset from CRAWDAD is used to evaluated the proposed system. A simulator is developed which simulates cruising behavior of taxies in the dataset and one virtual taxi which cruises based on our recommender system. Our simulation results indicate that although the statistics of historical data may be different from real-time passenger requests, our proposed recommender system is still effective on recommending better profitable cruising location.