Spatial–temporal travel pattern mining using massive taxi trajectory data
Deep understanding of residents’ travel patterns would provide helpful insights into the mechanisms of many socioeconomic phenomena. With the rapid development of location-aware computing technologies, researchers have easy access to large quantities of travel data. As an important data source, taxi...
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| Veröffentlicht in: | Physica A Jg. 501; S. 24 - 41 |
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Elsevier B.V
01.07.2018
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Deep understanding of residents’ travel patterns would provide helpful insights into the mechanisms of many socioeconomic phenomena. With the rapid development of location-aware computing technologies, researchers have easy access to large quantities of travel data. As an important data source, taxi trajectory data are featured by their high quality, good continuity and wide distribution, making it suitable for travel pattern mining. In this paper, we use taxi trajectory data to study spatial–temporal characterization of urban residents’ travel patterns from two aspects: attractive areas and hot paths. Firstly, a framework of trajectory preprocessing, including data cleaning and extracting the taxi passenger pick-up/drop-off points, is presented to reduce the noise and redundancy in raw trajectory data. Then, a grid density based clustering algorithm is proposed to discover travel attractive areas in different periods of a day. On this basis, we put forward a spatial–temporal trajectory clustering method to discover hot paths among travel attractive areas. Compared with previous algorithms, which only consider the spatial constraint between trajectories, temporal constraint is also considered in our method. Through the experiments, we discuss how to determine the optimal parameters of the two clustering algorithms and verify the effectiveness of the algorithms using real data. Furthermore, we analyze spatial–temporal characterization of Chongqing residents’ travel pattern.
•We use taxi GPS data to analyze spatial–temporal feature of travel patterns.•A grid clustering algorithm is proposed to discover travel attractive areas.•A method of trajectory clustering is used to discover urban hot paths.•Temporal factor is taken into consideration in spatial-temporal clustering. |
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| AbstractList | Deep understanding of residents’ travel patterns would provide helpful insights into the mechanisms of many socioeconomic phenomena. With the rapid development of location-aware computing technologies, researchers have easy access to large quantities of travel data. As an important data source, taxi trajectory data are featured by their high quality, good continuity and wide distribution, making it suitable for travel pattern mining. In this paper, we use taxi trajectory data to study spatial–temporal characterization of urban residents’ travel patterns from two aspects: attractive areas and hot paths. Firstly, a framework of trajectory preprocessing, including data cleaning and extracting the taxi passenger pick-up/drop-off points, is presented to reduce the noise and redundancy in raw trajectory data. Then, a grid density based clustering algorithm is proposed to discover travel attractive areas in different periods of a day. On this basis, we put forward a spatial–temporal trajectory clustering method to discover hot paths among travel attractive areas. Compared with previous algorithms, which only consider the spatial constraint between trajectories, temporal constraint is also considered in our method. Through the experiments, we discuss how to determine the optimal parameters of the two clustering algorithms and verify the effectiveness of the algorithms using real data. Furthermore, we analyze spatial–temporal characterization of Chongqing residents’ travel pattern.
•We use taxi GPS data to analyze spatial–temporal feature of travel patterns.•A grid clustering algorithm is proposed to discover travel attractive areas.•A method of trajectory clustering is used to discover urban hot paths.•Temporal factor is taken into consideration in spatial-temporal clustering. |
| Author | Li, Hang Tan, Longyou Liu, Weining Zheng, Linjiang Zhao, Xin Chen, Li Xia, Dong |
| Author_xml | – sequence: 1 givenname: Linjiang surname: Zheng fullname: Zheng, Linjiang email: zlj_cqu@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing 400044, China – sequence: 2 givenname: Dong surname: Xia fullname: Xia, Dong organization: College of Computer Science, Chongqing University, Chongqing 400044, China – sequence: 3 givenname: Xin surname: Zhao fullname: Zhao, Xin organization: College of Computer Science, Chongqing University, Chongqing 400044, China – sequence: 4 givenname: Longyou surname: Tan fullname: Tan, Longyou organization: Chongqing Integrated Transport Hub Development Investment Co., Ltd, Chongqing 401121, China – sequence: 5 givenname: Hang surname: Li fullname: Li, Hang organization: Chongqing Integrated Transport Hub Development Investment Co., Ltd, Chongqing 401121, China – sequence: 6 givenname: Li surname: Chen fullname: Chen, Li organization: College of Computer Science, Chongqing University, Chongqing 400044, China – sequence: 7 givenname: Weining surname: Liu fullname: Liu, Weining organization: College of Computer Science, Chongqing University, Chongqing 400044, China |
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| Keywords | Hot path Spatial–temporal Attractive area Trajectory clustering Grid-based clustering |
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