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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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering Jg. 25; H. 1; S. 220 - 232 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
01.01.2013
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| ISSN: | 1041-4347, 1558-2191 |
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| 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 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. |
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| AbstractList | 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. |
| Author | Sun, Guangzhong Xie, Xing Zheng, Yu Yuan, Jing |
| Author_xml | – sequence: 1 givenname: Jing surname: Yuan fullname: Yuan, Jing email: yuanjing@mail.ustc.edu.cn organization: University of Science and Technology of China, Beijing – sequence: 2 givenname: Yu surname: Zheng fullname: Zheng, Yu email: yuzheng@microsoft.com organization: Microsoft Research Asia, Beijing – sequence: 3 givenname: Xing surname: Xie fullname: Xie, Xing email: xing.xie@microsoft.com organization: Microsoft Research Asia, Beijing – sequence: 4 givenname: Guangzhong surname: Sun fullname: Sun, Guangzhong email: gzsun@ustc.edu.cn organization: University of Science and Technology of China, Beijing |
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| SubjectTerms | 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 |
| Title | T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence |
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