The Community for Technology Leaders
Green Image
Issue No. 01 - Jan. (2013 vol. 25)
ISSN: 1041-4347
pp: 220-232
Xing Xie , Microsoft Research Asia, Beijing
Yu Zheng , Microsoft Research Asia, Beijing
Jing Yuan , University of Science and Technology of China, Beijing
Guangzhong Sun , University of Science and Technology of China, Beijing
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 data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.
Trajectory, Roads, Vehicles, Global Positioning System, Routing, Cities and towns, Meteorology, driving behavior, Spatial databases and GIS, data mining, GPS trajectory, driving directions
Xing Xie, Yu Zheng, Jing Yuan, Guangzhong Sun, "T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence", IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 220-232, Jan. 2013, doi:10.1109/TKDE.2011.200
103 ms
(Ver )