@article{t-drive-enhancing-driving-directions-with-taxi-drivers-intelligence, author = {Yuan, Nicholas Jing and Zheng, Yu and Xie, Xing and Sun, Guangzhong}, title = {T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence}, booktitle = {}, year = {2013}, month = {January}, abstract = { This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. 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 and customized route for end users. 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 = {IEEE TKDE}, url = {https://www.microsoft.com/en-us/research/publication/t-drive-enhancing-driving-directions-with-taxi-drivers-intelligence/}, address = {}, pages = {}, journal = {IEEE Transactions on Knowledge and Data Engineering}, volume = {}, chapter = {}, isbn = {}, }