DESTPRE : A Data-Driven Approach to Destination Prediction for Taxi Rides

With the wide use of mobile devices, predicting the destination of moving vehicles has become an increasingly important problem for location based recommendation systems and destination-based advertising. Most existing approaches are based on various Markov chain models, in which the historical trajectories are used to train the model and the top-k most probable destinations are returned. We identify certain limitations of the previous approaches. Instead, we propose a new data-driven framework, called DESTPRE, which is not based on a probabilistic model, but directly operates on the trajectories and makes the prediction. We make use of only historic trajectories, without individual identity information. Our design of DESTPRE, although simple, is a result of several useful observations from the real trajectory data. DESTPRE involves an index based on Bucket PR Quadtree and Minwise hashing, for efficiently retrieving similar trajectories, and a clustering on destinations for predictions. By incorporating some additional ideas, we show that the prediction accuracy can be further improved. We have conducted extensive experiments on real Beijing Taxi dataset. The experimental results demonstrate the effectiveness of DESTPRE.