@inproceedings{t-drive-driving-directions-based-on-taxi-trajectories, author = {Yuan, Jing and Zheng, Yu and Zhang, Chengyang and Xie, Wenlei and Xie, Xing and Sun, Guangzhong and Huang, Yan}, title = {T-Drive: Driving Directions Based on Taxi Trajectories}, booktitle = {}, year = {2010}, month = {November}, abstract = { GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure 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 devised 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 practically 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. Download the Trajectory Data }, publisher = {ACM SIGSPATIAL GIS 2010}, url = {https://www.microsoft.com/en-us/research/publication/t-drive-driving-directions-based-on-taxi-trajectories/}, address = {}, pages = {}, journal = {}, volume = {}, chapter = {}, isbn = {}, }