T-drive: driving directions based on taxi trajectories

GPS-equipped taxis can be regarded as mobile sensors prob-ing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a des-tination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajecto-ries of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given de-parture time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is de-vised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the prac-tically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60–70 % of the routes suggested by our method are faster than the competing methods, and 20 % of the routes share the same results. On average, 50 % of our routes are at least 20 % faster than the competing approaches