1,304
Views
48
CrossRef citations to date
0
Altmetric
Original Articles

Detection of dynamic activity patterns at a collective level from large-volume trajectory data

&
Pages 946-963 | Received 13 May 2013, Accepted 22 Nov 2013, Published online: 20 Jan 2014

References

  • Abraham, S. and Lal, P.S., 2010. Trajectory similarity of network constrained moving objects and applications to traffic security. In: H. Chen, et al., eds. Proceedings of Intelligence and security informatics: Pacific Asia workshop, PAISI 2010, 21 June 2010, Hyderabad. Berlin: Springer, 31–43.
  • Ahas, R., et al., 2010a. Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: case study with mobile positioning data. Transportation Research Part C, 18, 45–54.
  • Ahas, R., et al., 2010b. Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 17 (1), 3–27.
  • Alvares, L.O., et al., 2007a. Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: J. Grundy, et al., eds. Proceedings of tutorials, posters, panels and industrial contributions at the 26th international conference on conceptual modeling – ER 2007. Auckland, CRIPT, 83. Darlinghurst: ACS, 149–154.
  • Alvares, L.O., et al., 2007b. A model for enriching trajectories with sematic geographical information. In: Proceedings of the 15th international symposium on advances in Geographic Information Systems (ACM-GIS’07), 7–9 November 2007 Seattle, WA. New York, NY: ACM, 1–8.
  • Andrienko, G.G. and Andrienko, N.N., 2008. Spatio-temporal aggregation for visual analysis of movements. In: D. Ebert and T. Ertl, eds. 2008 IEEE symposium on visual analytics science & technology, 21–23 October 2008, Columbus, OH. Los Alamitos, CA: IEEE Computer Society, 51–58.
  • Anselin, L., 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27 (2), 93–115.
  • Anselin, L., 2005. Exploring spatial data with GeoDaTM: a workbook [online]. GeoDa center for geospatial analysis and computation. Available from: https://geodacenter.asu.edu/system/files/geodaworkbook.pdf [Accessed 13 February 2013].
  • Ashbrook, D. and Starner, T., 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7 (5), 275–286.
  • Brakatsoulas, S., Pfoser, D., and Tryfona, N., 2004. Modeling, storing, and mining moving object databases. In: Proceedings 8th international database engineering and applications symposium (IDEAS ‘04), 7–9 July 2004 Coimbra. IEEE, 68–77.
  • Buchin, K., et al., 2011. Detecting commuting patterns by clustering subtrajectories. International Journal of Computational Geometry & Applications, 21 (3), 253–282.
  • Calabrese, F., et al., 2011. Estimating origin-destination flows using mobile phone location Data. IEEE Pervasive Computing, 10 (4), 36–44.
  • Diggle, P.J. and Chetwynd, A.G., 1995. Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4 (2), 124–136.
  • Elnekave, S., Last, M., and Maimon, O., 2007. Incremental clustering of mobile objects. In: 2007 IEEE 23rd international conference on data engineering workshop, 15–20 April 2007 Istanbul, 585–592.
  • Fotheringham, A.S. and Wong, D.W.S., 1991. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23, 1025–1044.
  • Gambs, S., Killijian, M.-O., and del Prado Cortez, M.N., 2010. GEPETO: a GEoPrivacy-Enhancing TOolkit. In: IEEE 24th international conference on advanced information networking & applications workshops (WAINA), 20–23 April 2010 Perth. IEEE, 1071–1076.
  • Gao, Y., et al., 2010. Algorithms for constrained k-nearest neighbor queries over moving object trajectories. Geoinformatica, 14 (2), 241–276.
  • Giannotti, F., et al., 2009. Mining mobility behavior from trajectory data. In: 2009 international conference on computational science and engineering, 29–31 August 2009 Vancouver, BC. 948–951.
  • Gidófalvi, G. and Pedersen, T., 2009. Mining long, sharable patterns in trajectories of moving objects. Geoinformatica, 13 (1), 27–55.
  • Gong, H., et al., 2012. A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 36 (2), 131–139.
  • Herring, R.J., 2010. Real-time traffic modeling and estimation with streaming probe data using machine learning. Thesis (PhD). University of California, Berkeley, CA.
  • Hu, J., et al., 2009. Dynamic modeling of urban population travel behavior based on data fusion of mobile phone positioning data and FCD. In: 2009 17th international conference on Geoinformatics, 12–14 August 2009 Fairfax, CA, 1–5.
  • Hu, H., et al., 2012. Pick-up tree based route recommendation from taxi trajectories. Web-Age Information Management: Lecture Notes in Computer Science, 7418, 471–483.
  • Hwang, R.-H., Hsueh, Y.-L., and Chung, H.-W., 2012. A novel time-obfuscated algorithm for trajectory privacy. In: 2012 international symposium on pervasive systems, algorithms and networks, 13–15 December 2012 San Marcos, TX. IEEE, 208–215.
  • Jeung, H., et al., 2010. Path prediction and predictive range querying in road network databases. The VLDB Journal, 19 (4), 585–602.
  • Knox, E.G. and Bartlett, M.S., 1964. The detection of space-time interactions. Journal of the Royal Statistical Society Series C (Applied Statistics), 13 (1), 25–30.
  • Kulldorff, M., 2010. SaTScanTM user guide for version 9.0 [online]. SaTScan. Available from: http://www.satscan.org/cgi-bin/satscan/register.pl/Current%20Version:%20SaTScan%20v9.1.1%20released%20March%209%202011.?todo=process_userguide_download [Accessed 10 February 2013].
  • Kulldorff, M., et al., 2005. A space-time permutation scan statistic for disease outbreak detection. PLoS medicine, 2 (3), 216–224.
  • Kwan, M.-P., 2000. Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C, 8 (1), 185–203.
  • Kwan, M.-P., Xiao, N., and Ding, G., 2014. Assessing activity pattern similarity with multidimensional sequence alignment based on a multiobjective optimization evolutionary algorithm. Geographical Analysis (forthcoming).
  • Lee, D., Baek, S., and Bae, H., 2009. aCN-RB-tree: update method for spatio-temporal aggregation of moving object trajectory in ubiquitous environment. In: 2009 international conference on computational science and its applications (ICCSA 2009), 29 June–2 July 2009, Yongin. Los Alamitos, CA: IEEE Computer Society, 177–182.
  • Lee, J., Han, J., and Whang, K., 2007. Trajectory clustering: a partition-and-group framework. In: 2007 ACM SIGMOD international conference on management of data (SIGMOD’ 07), 11–14 June 2007 Beijing. 1–12.
  • Li, X. and Lin, H., 2006. Indexing network-constrained trajectories for connectivity-based queries. International Journal of Geographical Information Science, 20 (3), 303–328.
  • Lin, B. and Su, J., 2008. One way distance: for shape based similarity search of moving object trajectories. Geoinformatica, 12 (2), 117–142.
  • Liu, X. and Karimi, H.A., 2006. Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30 (6), 741–756.
  • Lu, Y. and Liu, Y., 2012. Pervasive location acquisition technologies: opportunities and challenges for geospatial studies. Computers, Environment and Urban Systems, 36 (2), 105–108.
  • Manso, J.A.R.C., et al., 2010. DB-SMoT: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems (IS), 7–9 July 2010 London, 114–119.
  • Masciari, E., 2009. A framework for trajectory clustering. In: N. Trigoni, A. Markham, and S. Nawaz, eds. Proceedings of geosensor networks: third international conference, GSN 2009, 13–14 July 2009, Oxford. Berlin: Springer, 102–111.
  • Palma, A.T., et al., 2008. A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing (SAC’08), 16–20 March 2008 Fortaleza. New York, NY: ACM, 863–868.
  • Pfoser, D. and Jensen, C.S., 2005. Trajectory indexing using movement constraints. GeoInformatica, 9 (2), 93–115.
  • Ripley, B.D., 1976. The second-order analysis of stationary point processes. Journal of Applied Probability, 13 (2), 255–266.
  • Roh, G. and Hwang, S., 2010. NNCluster: an efficient clustering algorithm for road network trajectories. In: H. Kitagawa, et al., eds. Proceedings of database systems for advanced applications: 15th international conference, DASFAA 2010, part II, 1–4 April 2010, Tsukuba. Berlin: Springer, 47–61.
  • Ruan, T., Wang, Z.Q., and Song, T., 2009. A collaborative car auto-navigation framework based on intelligent trajectory mining. In: J. Zhang, G. Li, and J.Y. Yang, eds. 2009 international joint conference on bioinformatics, systems biology and intelligent computing (IJCBS ’09), 3–5 August 2009, Shanghai. Los Alamitos, CA: IEEE Computer Society, 591–596.
  • Scellato, S., et al., 2011. NextPlace: a spatio-temporal prediction framework for pervasive systems. Pervasive Computing: Lecture Notes in Computer Science, 6696, 152–169.
  • Shen, Y., Kwan, M.-P., and Chai, Y., 2013. Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China. Journal of Transport Geography, 32 (1), 1–11.
  • Shoval, N. and Isaacson, M., 2007. Sequence alignment as a method for human activity analysis in space and time. Annals of the Association of American Geographers, 97 (2), 282–297.
  • Wartenberg, D., 1985. Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Analysis, 17, 263–283.
  • Wei, L., et al., 2009. Exploring spatio-temporal features for traffic estimation on road networks. In: Advances in spatial & temporal databases: 11th international symposium (SSTD 2009), 8–10 July 2009 Aalborg, 399–404.
  • Yue, Y., et al., 2009. Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In: 17th international conference on Geoinformatics, 12–14 August 2009 Fairfax, VA, 1–6.
  • Zhao, X. and Xu, W., 2009. A clustering-based approach for discovering interesting places in a single trajectory. In: 2009 second international conference on intelligent computation technology & automation (ICICTA ‘09), 10–11 October 2009 Zhangjiajie. IEEE, 429–432.
  • Zheng, Y., et al., 2008. Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on ubiquitous computing (UbiComp’08), 21–24 September 2008 Seoul. New York, NY: ACM, 1–10.
  • Zheng, Y., et al., 2009. Mining interesting locations and travel sequences from GPS trajectories. In: 18th international world wide web conference (WWW 2009), 20–24 April 2009 Madrid. New York, NY: ACM, 1–10.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.