Lian Huang
ABSTRACT
GPS (Globe Positioning System) trajectory data provide a new way for city travel analysis others than traditional travel diary data. But generally raw GPS traces do not include information on trip purposes or activities. Earlier studies addressed this issue through a combination of manual and computer-assisted data processing steps. Nevertheless, geographic context databases provide the possibility for automatic activity identification based on GPS trajectories since each activity is uniquely defined by a set of features such as location and duration. Distinguished with most existing methods using two dimensional factors, this paper presents a novel approach using spatial temporal attractiveness of POIs (Point of Interests) to identify activity-locations as well as durations from raw GPS trajectory. We also introduce an algorithm to figure out how the intersections of trajectories and spatial-temporal attractiveness prisms indicate the potential possibilities for activities. Finally, Experiments using real world GPS tracking data, road networks and POIs are conducted for evaluations of the proposed approach.
- Wagner, D. P. Lexington Area Travel Data Collection Test: GPS for Personal Travel Surveys. Final Report. Office of Highway Policy Information and Office of Technology Applications, Battelle Transport Division (FHWA, Columbus, Ohio, Sept. 1997)Google Scholar
- Jean Wolf, Randall Guensler, William Bachman, Elimination of the Travel Diary: An Experiment to Derive Trip Purpose From GPS Travel Data, 2001. Transportation Research Board 80th Annual Meeting (January 7-11, 2001).Google Scholar
- Stefan Schönfelder and Kay W. Axhausen, Exploring the potentials of automatically collected GPS data for travel behaviour analysis- A Swedish data source, 2002, GI-Technologien für Verkehr und Logistik, 13, pp.155--179(2002)Google Scholar
- Kay W. Axhausen, Stefan Schönfelder, J Wolf, M. Oliveira, U Samaga, 80 weeks of GPS-traces: Approaches to enriching the trip information, 2003, 83rd Transportation Research Board Meeting(2003).Google Scholar
- B. Kochan, T. Bellemans, D. Janssens and G. Wets, Dynamic activity-travel data collection using a GPS-enabled personal digital assistant, 2006, Proceedings of the ninth International Conference on AATT, pp.319--324.Google Scholar
- Nadine Schuessler and Kay W. Axhausen, Processing Raw Data from Global Positioning Systems Without Additional Information, 2009, Transportation Research Record: Journal of Transportation Research Board, Issue 2105, pp.28--36 (2009).DOI= http://dx.doi.org/10.3141/2105-04.Google ScholarCross Ref
- Harvey J. Miller, Tobler's First Law and Spatial Analysis, 2004, Annals of the Association of American Geographers, 1467-8306, Volume 94, Issue 2, pp. 284--289( 2004) DOI= http://dx.doi.org/10.1111/j.1467-8306.2004.09402005.xGoogle Scholar
- Harvey J. Miller, Activities in space and time, P. Stopher, K. Button, K. Haynes and D. Hensher (eds.) Handbook of Transport 5: Transport Geography and Spatial Systems, 2004, Pergamon/Elsevier Science (2004).Google Scholar
- Terry Griffin, Yan Huang, A Decision Tree Classification Model to Automate Trip Purpose Derivation, 2005, CAINE: 44-49(2005)Google Scholar
- Daniel Ashbrook, Thad Starner, Using GPS to learn significant locations and predict movement across multiple users, Personal and Ubiquitous Computing, Volume 7, pp.275--286 (2003). DOI= http://dx.doi.org/10.1007/s00779-003-0240-0 Google ScholarDigital Library
- Wendy Bohte, Kees Maat, Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands, Transportation Research Part C, Volume 17, pp.285--297(2009) DOI= http://dx.doi.org/10.1016/j.trc.2008.11.004Google ScholarCross Ref
- Kexin Xie, Ke Deng, Xiaofang Zhou. From Trajectories to Activities: A Spatio-Temporal Join Approach. 2009. In Proceedings of the 2009 International Workshop on Location Based Social Networks (Seattle, Washington, The USA, 2009). CHI '00. ACM, New York, NY, 25-32 DOI=http://doi.acm.org/10.1145/1629890.1629897 Google ScholarDigital Library
- Lian Huang, Qingquan Li, Automatic activity identification from raw GPS vehicle tracking data, 2010, Proceedings of the Canadian Geomatics Conference.(2010)Google Scholar
- Donald J. Patterson, Lin Liao, Dieter Fox, and Kautz, 2003, Inferring High -- Level Behaviour from Low-Level Sensors, Ubiquitous Computing, 5th International Conference (Seattle, WA, USA, October 12-15, 2003) DOI= http://dx.doi.org/10.1007/978-3-540-39653-6_6Google Scholar
- Lin Liao, Dieter Fox and Henry Kautz, Location-Based Activity Recognition using Relational Markov Networks, Proceedings of the International Joint Conference on Artificial Intelligence (2005) Google ScholarDigital Library
- Christoph Teller, Thomas Reutterer, The evolving concept of retail attractiveness: What makes retail agglomerations attractive when customers shop at them?, Journal of Retailing and Consumer Services 15 (2008) 127--143, DOI=http://dx.doi.org/10.1016/j.jretconser.2007.03.003Google ScholarCross Ref
- Dick Ettema, Fabian Bastin, John Polak, Olu Ashiru. 2007. Modelling the joint choice of activity timing and duration. Transportation Research Part A. 41 (2007) 827--841. DOI= http://dx.doi.org/10.1016/j.tra.2007.03.001Google Scholar
- James, Jixian Wang, Timing Utility of daily Activities and Its Impact on Travel, 1996, Transportation Research part A, Volume. 30, Issue. 3, pp. 189--206(1996)Google Scholar
Index Terms
- Activity identification from GPS trajectories using spatial temporal POIs' attractiveness
Recommendations
Mining interesting locations and travel sequences from GPS trajectories
WWW '09: Proceedings of the 18th international conference on World wide webThe increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we ...
Learning Location Correlation from GPS Trajectories
MDM '10: Proceedings of the 2010 Eleventh International Conference on Mobile Data ManagementPeople’s location histories imply the location correlation that states the relations between geographical locations in the space of human behavior. With the correlation, we can enable many valuable services, such as location recommendation and sales ...
Adaptable Data-Driven Geofences for Notifying Points of Interest Using Tourists' GPS Trajectories
LocalRec '23: Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and GeoadvertisingA circular geofence (CGF), a virtual boundary defined by a center point and a radius, plays a significant role in various location-based services. Our paper discusses the need for automatic generation of geofences due to the difficulties in creating ...
Comments