Identifying the Stay Point Using GPS Trajectory of Taxis

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With the widespread use of personal mobile communications location-aware devices, a large amount of data of trajectory produced and can be used in information services. These huge amounts of data involves the pattern of human behavior information and cause numerous researchers' research interests. As is known,the key to travel information mining from the trajectory data is the stay point recognition and semantic annotation.Overcoming the shortcomings on adaptability and resistance to noise exists in existed stay points identification methods, and also combined with the basic characteristics of the taxi GPS data,We proposed a way with an parameter optimization stratage to get the stay points from a single trajectory and the figure shows it really works well, with high precision and strong adaptability on the recall ratio and precision ratio.And then,based on this significant achievements,we applies a refined clustering method based on the clustering radius and frequency parameters and get the POI results.

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3511-3515

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Yang Ye , Yu Zheng , Yukun Chen , Jianhua Feng , Xing Xie, Mining Individual Life Pattern Based on Location History, Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp.1-10, May 18-20, (2009).

DOI: 10.1109/mdm.2009.11

Google Scholar

[2] Yu Zheng , Like Liu , Longhao Wang , Xing Xie, Learning transportation mode from raw gps data for geographic applications on the web, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China.

DOI: 10.1145/1367497.1367532

Google Scholar

[3] Yu Zheng , Longhao Wang , Ruochi Zhang , Xing Xie , Wei-Ying Ma, GeoLife: Managing and Understanding Your Past Life over Maps, Proceedings of the The Ninth International Conference on Mobile Data Management, pp.211-212, April 27-30, (2008).

DOI: 10.1109/mdm.2008.20

Google Scholar

[4] Spaccapietra S, Parent C, Damiana M L, et al. A conceptual view on trajectories[J]. Data & Knowledge Engineering, 2008, 65: 126-146.

DOI: 10.1016/j.datak.2007.10.008

Google Scholar

[5] Nadine Schuessler, Kay W. Axhausen. Processing raw data from Global Positioning Systems without additional information. Transportation Research Record: Journal of the Transportation Research28-36 [J]. 2009. 10. 6.

DOI: 10.3141/2105-04

Google Scholar

[6] Andrey Tietbohl Palma,Vania Bogorny,Bart Kuijpers,et al. A clustering-based approach for discovering interesting places in trajectories[A]. Procedings of the ACM Symposium on Applied Computer, Advances inSpatial and Image-Based Information Systems Track[C], Fortaleza, Brazil, 16-20 March, 2008, pp.863-868.

DOI: 10.1145/1363686.1363886

Google Scholar

[7] Jianhe Du,Lisa Aultman-Hall. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues[J]. Transportation Research Part A, 2007, 41: 220-232.

DOI: 10.1016/j.tra.2006.05.001

Google Scholar

[8] Zhihua Zhang, Minhe Ji. Hierarchical segmentation for identifying activity stop from GPS trajectories. IEEE Transactions on Engineering Management, 2010, 57: 9-21.

Google Scholar