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T-Drive: Driving directions based on taxi trajectories

By Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun and Yan Huang

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 realworld 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

Topics: H.2.8. [Database applications, Data mining, Spatial databases and GIS. General Terms Algorithms, Experimentation
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.5526
Provided by: CiteSeerX
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