Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun
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.
In IEEE Transactions on Knowledge and Data Engineering (TKDE)
Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun. Driving with Knowledge from the Physical World, Association for Computing Machinery, Inc., 24 August 2011.
Yu Zheng. T-Drive trajectory data sample, 12 August 2011.
Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, and Yan Huang. T-Drive: Driving Directions Based on Taxi Trajectories, Association for Computing Machinery, Inc., 1 November 2010.