An effective taxi recommender system based on a spatio-temporal factor analysis model

The taxi fleet management systems based on GPS have become an important tool for taxi businesses. Such systems can be used not only for fleet management, but also to provide useful information for taxi drivers to increase their profits by mining historical GPS trajectories. In this paper, we propose a taxi recommender system for determining the next cruising location, which could be a value-added module in fleet management systems. In the literature, three factors have been considered in different studies to address a similar objective: distance between the current location and the recommended location, waiting time for the next passengers, and expected fare for the trip. In this paper, in addition to these factors, we consider one key factor based on driver experience: what 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 an OFF–ON model, is adopted to capture the relation between the passenger drop-off location and the next passenger get-on location. We also adopt an ON–OFF model to estimate the expected fare for a trip that begins at a recommended location. A real-world dataset from CRAWDAD is used to evaluate the proposed system. A simulator that simulates the cruising behavior of taxies in the dataset and a virtual taxi that cruises based on our recommender system is developed. Our simulation results indicate that although the statistics of the historical data may be different from real-time passenger requests, our recommender system is still effective in terms of recommending more profitable cruising locations.