T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence

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...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 25; no. 1; pp. 220 - 232
Main Authors: Yuan, Jing, Zheng, Yu, Xie, Xing, Sun, Guangzhong
Format: Journal Article
Language:English
Published: IEEE 01.01.2013
Subjects:
Cities and towns
data mining
Driving behavior
driving directions
Geographic Information Systems
Global Positioning System
GPS trajectory
Meteorology
Road vehicles
Spatial databases
Spatial databases and GIS
Trajectory
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2011.200