Abstract
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
- O. Abul, F. Bonchi, and M. Nanni. 2008. Never walk alone: Uncertainty for anonymity in moving objects databases. In Proceedings of the 24th IEEE International Conference on Data Engineering. IEEE, 376--385. Google ScholarDigital Library
- C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. 2003. A framework for clustering evolving data streams. In Proceedings of the 29th International Conference on Very Large Data Bases. VLDB Endowment 29, 81--92. Google ScholarDigital Library
- R. Agrawal, C. Faloutsos, and A. Swami. 1993. Efficient similarity search in sequence databases. Springer, 69--84. Google ScholarDigital Library
- H. Alt, A. Efrat, G. Rote, and C. Wenk. 2003. Matching planar maps. Journal of Algorithms 49, 2 (2003), 262--283. Google ScholarDigital Library
- J. Bao, Y. Zheng, and M. F. Mokbel. 2012. Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 199--208. Google ScholarDigital Library
- J. Bao, Y. Zheng, D. Wilkie, and M. F. Mokbel. 2015. A survey on recommendations in location-based social networks. GeoInformatica, 19, 3, 525--565. Google ScholarDigital Library
- R. Bellman. 1961. On the approximation of curves by line segments using dynamic programming. Communications of the ACM 4, 6 (1961), 284. Google ScholarDigital Library
- A. R. Beresford and F. Stajano. 2003. Location privacy in pervasive computing. IEEE Pervasive Computing 2, 1 (2003), 46--55. Google ScholarDigital Library
- S. Brakatsouls, D. Pfoser, R. Salas, and C. Wenk. 2005. On map-matching vehicle tracking data. In Proceedings of the 31st International Conference on Very Large Data Bases. VLDB Endowment, 853--864. Google ScholarDigital Library
- T. Brinkhoff and O. Str, 2002. A framework for generating network-based moving objects. Geoinformatica, 6, 2 (2002), 153--180. Google ScholarDigital Library
- H. Cao, N. Mamoulis, and D. W. Cheung. 2005. Mining frequent spatio-temporal sequential patterns. In Proceedings of the 5th IEEE International Conference on Data Mining. IEEE, 82--89. Google ScholarDigital Library
- H. Cao, N. Mamoulis, and D. W. Cheung. 2007. Discovery of periodic patterns in spatiotemporal sequences. IEEE Transactions on Knowledge and Data Engineering 19, 4 (2007), 453--467. Google ScholarDigital Library
- I. V. Cadez, S. Gaffney, and P. Smyth. 2000. A general probabilistic framework for clustering individuals and objects. In Proceedings of the 6th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 140--149. Google ScholarDigital Library
- V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly detection: A survey. ACM Computing Surveys 41, 3 (2009), 1--58. Google ScholarDigital Library
- S. Chawla, Y. Zheng, and J. Hu. 2012. Inferring the root cause in road traffic anomalies. In Proceedings of the 12th IEEE International Conference on Data Mining. IEEE, 141--150. Google ScholarDigital Library
- S. S. Chawathe. 2007. Segment-based map matching. 2007 IEEE Intelligent Vehicles Symposium. IEEE, 1190--1197.Google ScholarCross Ref
- Y. Chen, K. Jiang, Y. Zheng, C. Li, and N. Yu. 2009. Trajectory simplification method for location-based social networking services. In Proceedings of the ACM SIGSPATIAL Workshop on Location-Based Social Networking Services. ACM, 33--40. Google ScholarDigital Library
- L. Chen and R. Ng. 2004. On the marriage of lp-norms and edit distance. In Proceedings of the 30th International Conference on Very Large Data Bases. VLDB Endowment, 792--803. Google ScholarDigital Library
- L. Chen, M. T. Ozsu, and V. Oria. 2005. Robust and fast similarity search for moving object trajectories. In Proceedings of the 24th ACM SIGMOD International Conference on Management of Data. ACM, 491--502. Google ScholarDigital Library
- Z. Chen, H. T. Shen, and X. Zhou. 2011. Discovering popular routes from trajectories. In Proceedings of the 27th IEEE International Conference on Data Engineering. IEEE, 900--911. Google ScholarDigital Library
- Z. Chen, H. T. Shen, X. Zhou, Y. Zheng, and X. Xie. 2010. Searching trajectories by locations—An efficient study. In Proceedings of the 29th ACM SIGMOD International Conference on Management of Data. ACM, 255--266. Google ScholarDigital Library
- W. Chen, M. Yu, Z. Li, and Y. Chen. 2003. Integrated vehicle navigation system for urban applications. In Proceedings of the International Conference Global Navigation Satellite System. CGNS, 15--22.Google Scholar
- R. Cheng, J. Chen, M. F. Mokbel, and C. Y. Chow. 2008. Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. In Proceedings of the IEEE 24th Conference on Data Engineering. IEEE, 973--982. Google ScholarDigital Library
- R. Cheng, D. V. Kalashnikov, and S. Prabhakar. 2004. Querying imprecise data in moving objects environments. IEEE Transactions on Knowledge and Data Engineering 16, 9 (2004). Google ScholarDigital Library
- C. Y. Chow and M. F. Mokbel. 2011. Privacy of spatial trajectories. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 109--141.Google Scholar
- A. Civilis, C. S. Jensen, J. Nenortaite, and S. Pakalnis. 2005. Techniques for efficient road-network-based tracking of moving objects. IEEE Transactions on Knowledge and Date Engineering 17, 5 (2005), 698--711. Google ScholarDigital Library
- K. Deng, K. Xie, K. Zheng, and X. Zhou. 2011. Trajectory indexing and retrieval. Computing with Spatial Trajectories. Y. Zheng and X. Zhou (Eds.). Springer, 35--60.Google Scholar
- D. Douglas and T. Peucker. 1973. Algorithms for the reduction of the number of points required to represent a line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization 10, 2 (1973), 112--122.Google ScholarCross Ref
- C. Duntgen, T. Behr, and R. H. Guting. 2009. BerlinMOD: A benchmark for moving object databases. The VLDB Journal 18, 6 (2009), 1335--1368. Google ScholarDigital Library
- T. Emrich, H. P. Kriegel, N. Mamoulis, M. Renz, and A. Züfle. 2012. Querying uncertain spatio-temporal data. In Proceedings of the 28th IEEE International Conference on Data Engineering. IEEE, 354--365. Google ScholarDigital Library
- Y. Fu, Y. Ge, Y. Zheng, Z. Yao, Y. Liu, H. Xiong, and N. J. Yuan. 2014a. Sparse real estate ranking with online user reviews and offline moving behaviors. In Proceedings of the 14th IEEE International Conference on Data Mining. IEEE, 120--129. Google ScholarDigital Library
- Y. Fu, H. Xiong, Y. Ge, Z. Yao, and Y. Zheng. 2014b. Exploiting geographic dependencies for real estate appraisal: A mutual perspective of ranking and clustering. In Proceedings of the 20th SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 1047--1056. Google ScholarDigital Library
- S. Gaffney and P. Smyth. 1999. Trajectory clustering with mixtures of regression models. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 63--67. Google ScholarDigital Library
- F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli. 2007. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 330--339. Google ScholarDigital Library
- G. Gid’ofalvi, X. Huang, and T. B. Pedersen. 2007. Privacy-preserving data mining on moving object trajectories. In Proceedings of the 8th IEEE International Conference on Mobile Data Management. IEEE, 60--68. Google ScholarDigital Library
- J. S. Greenfeld. 2002. Matching GPS observations to locations on a digital map. In Proceedings of the 81st Annual Meeting of the Transportaion Research Board. 576--582.Google Scholar
- J. Gudmundsson and M. V. Kreveld. 2006. Computing longest duration flocks in trajectory data. In Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems. ACM, 35--42. Google ScholarDigital Library
- J. Gudmundsson, M. V. Kreveld, and B. Speckmann. 2004. Efficient detection of motion patterns in spatio-temporal data sets. In Proceedings of the 12th Annual ACM International Symposium on Advances in Geographic Information Systems. ACM, 250--257. Google ScholarDigital Library
- J. Hershberger and J. Snoeyink. 1992. Speeding up the Douglas-Peucker line simplification algorithm. In Proceedings of the International Symposium on Spatial Data Handling. 134--143.Google Scholar
- B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. 2010. Achieving guaranteed anonymity in GPS traces via uncertainty-aware path cloaking. IEEE Transactions on Mobile Computing 9, 8 (2010), 1089--1107. Google ScholarDigital Library
- C. S. Jensen, D. Lin, and B. C. Ooi. 2007. Continuous clustering of moving objects. IEEE Transaction on Knowledge and Data Engineering 19, 9 (2007), 1161--1174. Google ScholarDigital Library
- H. Jeung, H. Shen, and X. Zhou. 2008a. Convoy queries in spatio-temporal databases. In Proceedings of the 24th IEEE International Conference on Data Engineering. IEEE, 1457--1459. Google ScholarDigital Library
- H. Jeung, M. L. Yiu, and C. S. Jensen. 2011. Trajectory pattern mining. Computing with Spatial Trajectories. Y. Zheng and X. Zhou (Eds.). Springer, 143--177.Google Scholar
- H. Jeung, M. Yiu, X. Zhou, C. Jensen, and H. Shen. 2008b. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment 1, 1 (2008), 1068--1080. Google ScholarDigital Library
- G. Kellaris, N. Pelekis, and Y. Theodoridis. 2009. Trajectory compression under network constraints. In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases. 392--398. Google ScholarDigital Library
- E. J. Keogh, S. Chu, D. Hart, and M. J. Pazzani. 2001. An on-line algorithm for segmenting time series. In Proceedings of the IEEE International Conference on Data Engineering. IEEE, 289--296. Google ScholarDigital Library
- A. Kharrat, I. S. Popa, K. Zeitouni, and S. Faiz. 2008. Clustering algorithm for network constraint trajectories. Headway in Spatial Data Handling. 631--647.Google Scholar
- H. Kido, Y. Yanagisawa, and T. Satoh. 2005. An anonymous communication technique using dummies for location-based services. In Proceedings of the 3rd International Conference on Pervasive Services. IEEE, 88--97.Google Scholar
- J. Krumm. 2011. Trajectory analysis for driving. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 213--241.Google Scholar
- J. Krumm and E. Horvitz. 2004. LOCADIO: Inferring motion and location from Wi-Fi signal strengths. In Proceedings of the International Conference on Mobile and Ubiquitous Systems. IEEE, 4--13.Google Scholar
- J. G. Lee, J. Han, and K. Y. Whang. 2007. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM SIGMOD Conference on Management of Data. ACM, 593--604. Google ScholarDigital Library
- J. Lee, J. Han, and X. Li. 2008. Trajectory outlier detection: A partition-and-detect framework. In Proceedings of the 24th IEEE International Conference on Data Engineering. IEEE, 140--149. Google ScholarDigital Library
- W.-C. Lee and J. Krumm. 2011. Trajectory preprocessing. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 1--31.Google Scholar
- Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and M. Ma. 2008. Mining user similarity based on location history. In Proceedings of the 16th Annual ACM International Conference on Advances in Geographic Information Systems. ACM, 34. Google ScholarDigital Library
- Z. Li, B. Ding, J. Han, and R. Kays. 2010a. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment 3, 1--2 (2010), 723--734. Google ScholarDigital Library
- Z. Li, J. Lee, X. Li, and J. Han. 2010b. Incremental clustering for trajectories. Database Systems for Advanced Applications. 32--46. Google ScholarDigital Library
- Z. Li, B. Ding, J. Han, R. Kays, and P. Nye. 2010c. Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1099--1108. Google ScholarDigital Library
- Z. Li, J. Wang, and J. Han. 2012. Mining event periodicity from incomplete observations. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 444--452. Google ScholarDigital Library
- L. Liao, D. Fox, and H. Kautz. 2004. Learning and inferring transportation routines. In Proceedings of the National Conference on Artificial Intelligence. 348--353. Google ScholarDigital Library
- S. Liu, K. Jayarajah, A. Misra, and R. Krishnan. 2013. TODMIS: Mining communities from trajectories. In Proceedings of the 22nd ACM CIKM International Conference on Information and Knowledge Management. ACM, 2109--2118. Google ScholarDigital Library
- S. Liu, L. Ni, and R. Krishnan. 2014. Fraud detection from Taxis’ driving behaviors. IEEE Transactions on Vehicular Technology 63, 1 (2014), 464--472.Google ScholarCross Ref
- W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xie. 2011. Discovering spatio-temporal causal interactions in traffic data streams. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1010--1018. Google ScholarDigital Library
- Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. 2009. Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Geographical Information Systems. ACM, 352--361. Google ScholarDigital Library
- W. Luo, H. Tan, L. Chen, and M. N. Lionel. 2013. Finding time period-based most frequent path in big trajectory data. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 713--724. Google ScholarDigital Library
- S. Ma, Y. Zheng, and O. Wolfson. 2013. T-Share: A large-scale dynamic taxi ridesharing service. In Proceedings of the 29th IEEE International Conference on Data Engineering. IEEE, 410--421. Google ScholarDigital Library
- S. Ma, Y. Zheng, and O. Wolfson. 2015. Real-time city-scale taxi ridesharing. IEEE Transactions on Knowledge and Data Engineering 99. DOI: http://doi.ieeecomputersociety.org/10.1109/TKDE.2014.2334313Google Scholar
- N. Maratnia and R. A. de By. 2004. Spatio-temporal compression techniques for moving point objects. In Proceedings of the 9th International Conference on Extending Database Technology. 765--782.Google Scholar
- R. B. McMaster. 1986. A statistical analysis of mathematical measures of linear simplification. The American Cartographer 13, 2 (1986), 103--116.Google ScholarCross Ref
- M. F. Mokbel, C. Y. Chow, and W. G. Aref. 2007. The new Casper: Query processing for location services without compromising privacy. In Proceedings of the 23rd IEEE International Conference on Data Engineering. IEEE, 1499--1500.Google Scholar
- M. Mokbel, L. Alarabi, J. Bao, A. Eldawy, A. Magdy, M. Sarwat, E. Waytas, and S. Yackel. 2014. A demonstration of MNTG —A Web-based road network traffic generator. In Proceedings of the 30th IEEE International Conference on Data Engineering, IEEE, 1246--1249.Google Scholar
- A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. 2009. WhereNext: A location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 637--646. Google ScholarDigital Library
- M. E. Nergiz, M. Atzori, Y. Saygin, and B. Guc. 2009. Towards trajectory anonymization: A generalization-based approach. Transactions on Data Privacy 2, 1 (2009), 47--75. Google ScholarDigital Library
- P. Newson, J. Krumm. 2009. Hidden Markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Geographical Information Systems. ACM, 336--343. Google ScholarDigital Library
- J. Niedermayer, A. Zufle, T. Emrich, M. Renz, N. Mamouliso, L. Chen, and H. Kriegel. 2014. Probabilistic nearest neighbor queries on uncertain moving object trajectories. Proceedings of the VLDB Endowment 7, 3 (2014), 205--216. Google ScholarDigital Library
- W. Y. Ochieng, M. A. Quddus, and R. B. Noland. 2004. Map-matching in complex urban road networks. Brazilian Journal of Cartography 55, 2 (2004), 1--18.Google Scholar
- B. Pan, Y. Zheng, D. Wilkie, and C. Shahabi. 2013. Crowd sensing of traffic anomalies based on human mobility and social media. In Proceedings of the 21st Annual ACM International Conference on Advances in Geographic Information Systems. ACM, 334--343. Google ScholarDigital Library
- L. X. Pang, S. Chawla, W. Liu, and Y. Zheng. 2011. On mining anomalous patterns in road traffic streams. In Proceedings of the International Conference on Advanced Data Mining and Applications. 237--251. Google ScholarDigital Library
- L. X. Pang, S. Chawla, W. Liu, and Y. Zheng. 2013. On detection of emerging anomalous traffic patterns using GPS data. Data & Knowledge Engineering, 87 (2013), 357--373. Google ScholarDigital Library
- D. J. Patterson, L. Liao, D. Fox, and H. Kaut. 2003. Inferring high-level behavior from low-level sensors. In Proceedings of the 5th International Conference on Ubiquitous Computing. ACM, 73--89.Google Scholar
- J. Pei, J. Han, B. Mortazavi-Asl, and H. Pinto. 2011. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the 29th IEEE International Conference on Data Engineering. IEEE, 215. Google ScholarDigital Library
- D. Pfoser and C. S. Jensen. 1999. Capturing the uncertainty of moving objects representation. In Proceedings of the International Symposium on Advances in Spatial Databases. 111--131. Google ScholarDigital Library
- D. Pfoser, C. S. Jensen, and Y. Theodoridis. 2000. Novel approaches to the indexing of moving object trajectories. In Proceedings of the 26th International Conference on Very Large Data Bases. VLDB Endowment, 395--406. Google ScholarDigital Library
- O. Pink and B. Hummel. 2008. A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems. IEEE, 862--867.Google Scholar
- M. Potamias, K. Patroumpas, and T. Sellis. 2006. Sampling trajectory streams with spatio-temporal criteria. In Proceedings of the 18th International Conference on Scientific and Statistical Database Management. IEEE, 275--284. Google ScholarDigital Library
- S. Qiao, C. Tang, H. Jin, T. Long, S. Dai, Y. Ku, and M. Chau. 2010. Putmode: Prediction of uncertain trajectories in moving objects databases. Applied Intelligence 33, 3 (2010), 370--386. Google ScholarDigital Library
- M. A. Quddus, W. Y. Ochieng, and R. B. Noland. 2006. A high accuracy fuzzy logic-based map-matching algorithm for road transport. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10, 3 (2006), 103--115.Google ScholarCross Ref
- K. Richter, F. Schmid, and P. Laube. 2012. Semantic trajectory compression: Representing urban movement in a nutshell. Journal of Spatial Information Science, 4 (2012), 3--30.Google Scholar
- S. Rinzivillo, S. Mainardi, F. Pezzoni, M. Coscia, D. Pedreschi, and F. Giannotti. 2012. Discovering the geographical borders of human mobility. Künstl Intell. 26, 3 (2012), 253--260.Google ScholarCross Ref
- J. Shang, Y. Zheng, W. Tong, E. Chang, and Y. Yu. 2014. Inferring gas consumption and pollution emission of vehicles throughout a city. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1027--1036. Google ScholarDigital Library
- R. Song, W. Sun, B. Zheng, and Y. Zheng. 2014. PRESS: A novel framework of trajectory compression in road networks. Proceedings of the VLDB Endowment 7, 9 (2014), 661--672. Google ScholarDigital Library
- H. Su, K. Zheng, H. Wang, J. Huang, and X. Zhou. 2013. Calibrating trajectory data for similarity-based analysis. In Proceedings of the 39th International Conference on Very Large Data Bases. VLDB Endowment, 833--844. Google ScholarDigital Library
- Y. Tao and D. Papadias. 2001a. Efficient historical R-trees. In Proceedings of the 13th International Conference on Scientific and Statistical Database Management, 223--232. Google ScholarDigital Library
- Y. Tao and D. Papadias. 2001b. Mv3r-tree: A spatio-temporal access method for timestamp and interval queries. In Proceedings of the 27th International Conference on Very Large Data Bases. VLDB Endowment, 431--440. Google ScholarDigital Library
- Y. Tao, D. Papadias, and Q. Shen. 2002. Continuous nearest neighbour search. In Proceedings of the 28th International Conference on Very Large Data Bases. VLDB Endowment, 287--298. Google ScholarDigital Library
- L. A. Tang, Y. Zheng, X. Xie, J. Yuan, X. Yu, and J. Han. 2011. Retrieving k-nearest neighboring trajectories by a set of point locations. In Proceedings of the 12th Symposium on Spatial and Temporal Databases. Springer, 223--241. Google ScholarDigital Library
- L. A. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung, C. Hung, and W. Peng. 2012a. Discovery of traveling companions from streaming trajectories. In Proceedings of the 28th IEEE International Conference on Data Engineering. IEEE, 186--197. Google ScholarDigital Library
- L. A. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung, W. Peng, and T. L. Porta. 2012b. A framework of traveling companion discovery on trajectory data streams. ACM Transactions on Intelligent Systems and Technology 5, 1 (2012). Google ScholarDigital Library
- M. Terrovitis and N. Mamoulis. 2008. Privacy preservation in the publication of trajectories. In Proceedings the 9th IEEE International Conference on Mobile Data Management. IEEE, 65--72. Google ScholarDigital Library
- G. Trajcevski, A. N. Choudhary, O. Wolfson, L. Ye, and G. Li. 2010. Uncertain range queries for necklaces. In Proceedings of the 11th IEEE International Conference on Mobile Data Management. IEEE, 199--208. Google ScholarDigital Library
- G. Trajcevski, R. Tamassia, H. Ding, P. Scheuermann, and I. F. Cruz. 2009. Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology. ACM, 874--885. Google ScholarDigital Library
- G. Trajcevski, O. Wolfson, K. Hinrichs, and S. Chamberlain. 2004. Managing uncertainty in moving objects databases. ACM Transactions on Database Systems 29, 3(04), 463--507. Google ScholarDigital Library
- S. Timothy, A. Varshavsky, A. Lamarca, M. Y. Chen, and T. Chounhury. 2006. Mobility detection using everyday GSM traces. In Proceedings of the 8th International Conference on Ubiquitous Computing. ACM, 212--224. Google ScholarDigital Library
- L. Wang, Y. Zheng, X. Xie, and W. Ma. 2008. A flexible spatio-temporal indexing scheme for large-scale GPS track retrieval. In Proceedings of the 8th IEEE International Conference on Mobile Data Management. IEEE, 1--8. Google ScholarDigital Library
- Y. Wang, Y. Zheng, and Y. Xue. 2014. Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 25--34. Google ScholarDigital Library
- L. Wei, Y. Zheng, and W. Peng. 2012. Constructing popular routes from uncertain trajectories. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 195--203. Google ScholarDigital Library
- X. Xiao, Y. Zheng, Q. Luo, and X. Xie. 2010. Finding similar users using category-based location history. In Proceedings of the 18th Annual ACM International Conference on Advances in Geographic Information Systems. ACM, 442--445. Google ScholarDigital Library
- X. Xiao, Y. Zheng, Q. Luo, and X. Xie. 2014. Inferring social ties between users with human location history. Journal of Ambient Intelligence and Humanized Computing 5, 1 (2014), 3--19.Google ScholarCross Ref
- H. Xie, L. Kulik, and E. Tanin. 2010. Privacy-aware traffic monitoring. IEEE Transactions on Intelligent Transportation Systems 11, 1 (2010), 61--70. Google ScholarDigital Library
- C. Xu, Y. Gu, L. Chen, J. Qiao, and G. Yu. 2013. Interval reverse nearest neighbor queries on uncertain data with Markov correlations. In Proceedings of the 29th IEEE International Conference on Data Minning. IEEE, 170--181. Google ScholarDigital Library
- X. Xu, J. Han, and W. Lu. 1990. RT-tree: An improved R-Tree indexing structure for temporal spatial databases. In Proceedings of International Symposium on Spatial Data Handling. 1040--1049.Google Scholar
- A. Y. Xue, R. Zhang, Y. Zheng, X. Xie, J. Huang, and Z. Xu. 2013. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Proceedings of the 29th IEEE International Conference on Data Engineering. IEEE, 254--265. Google ScholarDigital Library
- X. Yan, J. Han, and R. Afshar. 2003. CloSpan: Mining closed sequential patterns in large datasets. In Proceedings of the 3rd SIAM International Conference on Data Mining. IEEE, 166--177.Google Scholar
- J. Yang, W. Wang, and P. S. Yu. 2003. Mining asynchronous periodic patterns in time series data. IEEE Transactions on Knowledge and Data Engineering 15, 3 (2003), 613--628. Google ScholarDigital Library
- J. Yang, W. Wang, and S. Y. Philip. 2001. Infominer: Mining surprising periodic patterns. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 395--400. Google ScholarDigital Library
- J. Yang, W. Wang, and P. S. Yu. 2002. Infominer+: Mining partial periodic patterns with gap penalties. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 725--728. Google ScholarDigital Library
- Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie. 2009. Mining individual life pattern based on location history. In Proceedings of the 10th IEEE International Conference on Mobile Data Management. IEEE, 1--10. Google ScholarDigital Library
- B. K. Yi, H. Jagadish, and C. Faloutsos. 1998. Efficient retrieval of similar time sequences under time warping. In Proceedings of the 14th IEEE International Conference on Data Engineering. IEEE, 201--208. Google ScholarDigital Library
- H. B. Yin and O. Wolfson. 2004. A weight-based map matching method in moving objects databases1. In Proceedings of the 16th International Conference on Scientific and Statistical Database Management. IEEE, 437--410. Google ScholarDigital Library
- J. Yin, X. Chai, and Q. Yang. 2004. High-level goal recognition in a wireless Lan. In Proceedings of the National Conference on Artificial Intelligence. AAAI, 578--584. Google ScholarDigital Library
- H. Yoon, Y. Zheng, X. Xie, and W. Woo. 2012. Social itinerary recommendation from user-generated digital trails. Journal on Personal and Ubiquitous Computing 16, 5 (2012), 469--484. Google ScholarDigital Library
- H. Yoon, Y. Zheng, X. Xie, and W. Woo. 2011. Smart itinerary recommendation based on user-generated GPS trajectories. In Proceedings of the 8th IEEE International Conference on Ubiquitous Intelligence and Computing. IEEE, 19--34. Google ScholarDigital Library
- J. Yuan, Y. Zheng, and X. Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 186--194. Google ScholarDigital Library
- J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. 2010a. T-Drive: Driving directions based on taxi trajectories. In Proceedings of the 18th Annual ACM International Conference on Advances in Geographic Information Systems. ACM, 99--108. Google ScholarDigital Library
- J. Yuan, Y. Zheng, X. Xie, and G. Sun. 2011a. Driving with knowledge from the physical world. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 316--324. Google ScholarDigital Library
- J. Yuan, Y. Zheng, X. Xie, and G. Sun. 2013a. T-Drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Transaction on Knowledge and Data Engineering 25, 1 (2013), 220--232. Google ScholarDigital Library
- J. Yuan, Y. Zheng, C. Zhang, X. Xie and G. Sun. 2010b. An interactive-voting based map matching algorithm. In Proceedings of the 11th IEEE International Conference on Mobile Data Management. IEEE, 43--52. Google ScholarDigital Library
- J. Yuan, Y. Zheng, L. Zhang, X. Xie, and G. Sun. 2011b. Where to find my next passenger? In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 109--118. Google ScholarDigital Library
- N. J. Yuan, Y. Zheng, and X. Xie. 2012. Segmentation of Urban Areas using Road Networks. Technical Report MSR-TR-2012-65.Google Scholar
- N. J. Yuan, Y. Zheng, L. Zhang, and X. Xie. 2013b. T-Finder: A recommender system for finding passengers and vacant taxis. IEEE Transaction on Knowledge and Data Engineering 25, 10 (2013), 2390--2403. Google ScholarDigital Library
- N. J. Yuan, Y. Zheng, X. Xie, Y. Wang, K. Zheng, and H. Xiong. 2015. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering 27, 3 (2015), 1041--4347.Google ScholarCross Ref
- D. Zhang, N. Li, Z. Zhou, C. Chen, L. Sun, and S. Li. 2011. iBAT: Detecting anomalous taxi trajectories from GPS traces. In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 99--108. Google ScholarDigital Library
- F. Zhang, D. Wilkie, Y. Zheng, and X. Xie. 2013. Sensing the pulse of urban refueling behavior. In Proceedings of the 15th International Conference on Ubiquitous Computing. ACM, 13--22. Google ScholarDigital Library
- F. Zhang, N. J. Yuan, D. Wilkie, Y. Zheng, and X. Xie. 2015. Sensing the pulse of urban refueling behavior: A perspective from taxi mobility. ACM Transactions on Intelligent Systems and Technology 6 (2015), 3. Google ScholarDigital Library
- K. Zheng, Y. Zheng, X. Xie, and X. Zhou. 2012a. Reducing uncertainty of low-sampling-rate trajectories. In Proceedings of the 28th IEEE International Conference on Data Engineering. IEEE, 1144--1155. Google ScholarDigital Library
- K. Zheng, Y. Zheng, N. J. Yuan, and S. Shang. 2013a. On discovery of gathering patterns from trajectories. In Proceedings of the 29th IEEE International Conference on Data Engineering. IEEE, 242--253. Google ScholarDigital Library
- K. Zheng, Y. Zheng, N. J. Yuan, S. Shang, and X. Zhou. 2014a. Online discovery of gathering patterns over trajectories. IEEE Transaction on Knowledge and Data Engineering 26, 8 (2014), 1974--1988.Google ScholarCross Ref
- V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang. 2010a. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of the 24th AAAI Conference on Artificial Intelligence. AAAI, 236--241.Google Scholar
- V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. 2010b. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th International Conference on World Wide Web. ACM, 1029--1038. Google ScholarDigital Library
- V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. 2012b. Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. Artificial Intelligence 184--185 (2012), 17--37. Google ScholarDigital Library
- Y. Zheng. 2011. Location-based social networks: users. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 243--276.Google ScholarDigital Library
- Y. Zheng. 2012. Tutorial on location-based social networks. In Proceedings of the 21st International Conference on World Wide Web. ACM.Google Scholar
- Y. Zheng, L. Capra, O. Wolfson, and H. Yang. 2014b. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 38--55. Google ScholarDigital Library
- Y. Zheng, X. Chen, Q. Jin, Y. Chen, X. Qu, X. Liu, E. Chang, W. Ma, Y. Rui, and W. Sun. 2014c. A Cloud-based knowledge discovery system for monitoring fine-grained air quality. MSR-TR-2014-40.Google Scholar
- Y. Zheng, Y. Chen, Q. Li, X. Xie, and W.-Y. Ma. 2010c. Understanding transportation modes based on GPS data for Web applications. ACM Transactions on the Web 4, 1 (2010), 1--36. Google ScholarDigital Library
- Y. Zheng, Y. Chen, X. Xie, and W.-Y. Ma. 2009a. GeoLife2.0: A location-based social networking service. In Proceedings of the 10th IEEE International Conference on Mobile Data Management. IEEE, 357--358. Google ScholarDigital Library
- Y. Zheng, S. E. Koonin, and O. E. Wolfson. 2013. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. ACM. Google Scholar
- Y. Zheng, Q. Li, Y. Chen, and X. Xie. 2008a. Understanding mobility based on GPS data. In Proceedings of the 11th International Conference on Ubiquitous Computing. ACM, 312--321. Google ScholarDigital Library
- Y. Zheng, L. Liu, L. Wang, and X. Xie. 2008b. Learning transportation mode from raw GPS data for geographic application on the Web. In Proceedings of the 17th International Conference on World Wide Web. ACM, 247--256. Google ScholarDigital Library
- Y. Zheng, F. Liu, and H. P. Hsieh. 2013b. U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 1436--1444. Google ScholarDigital Library
- Y. Zheng, T. Liu, Y. Wang, Y. Liu, Y. Zhu, and E. Chang. 2014c. Diagnosing New York City's noises with ubiquitous data. In Proceedings of the 16th ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 715--725. Google ScholarDigital Library
- Y. Zheng, Y. Liu, J. Yuan, and X. Xie. 2011a. Urban computing with taxicabs. In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 89--98. Google ScholarDigital Library
- Y. Zheng and X. Xie. 2011b. Learning travel recommendations from user-generated GPS traces. ACM Transactions on Intelligent Systems and Technology 2, 1 (2011), 2--19. Google ScholarDigital Library
- Y. Zheng, X. Xie, and W.-Y. Ma. 2010d. GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin 33, 2 (2010), 32--40.Google Scholar
- Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W.-Y. Ma. 2011c. Recommending friends and locations based on individual location history. ACM Transaction on the Web 5, 1 (2011), 5--44. Google ScholarDigital Library
- Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. 2009b. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web. ACM, 791--800. Google ScholarDigital Library
- Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. 2009c. Mining correlation between locations using human location history. In Proceedings of the 17th Annual ACM International Conference on Advances in Geographic Information Systems. ACM, 352--361. Google ScholarDigital Library
- Y. Zheng and X. Zhou. 2011. Computing with Spatial Trajectories. Springer. Google ScholarDigital Library
- Y. Zhu, Y. Zheng, L. Zhang, D. Santani, X. Xie, and Q. Yang. 2011. Inferring Taxi Status using GPS Trajectories. Technical Report MSR-TR-2011-144.Google Scholar
- GeoLife Data: http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx.Google Scholar
- T-Drive Data: http://research.microsoft.com/apps/pubs/?id=152883.Google Scholar
- Trajectory with transportation modes: http://research.microsoft.com/apps/pubs/?id=141896.Google Scholar
- User check-in data: https://www.dropbox.com/s/4nwb7zpsj25ibyh/check-in%20data.zip.Google Scholar
- Hurricane trajectory (HURDAT): http://www.nhc.noaa.gov/data/hurdat.Google Scholar
- The Greek Trucks Dataset,” http://www.chorochronos.org.Google Scholar
- Movebank data: https://www.movebank.org/.Google Scholar
Index Terms
- Trajectory Data Mining: An Overview
Recommendations
Clustering and aggregating clues of trajectories for mining trajectory patterns and routes
In this paper, we propose a new trajectory pattern mining framework, namely Clustering and Aggregating Clues of Trajectories (CACT), for discovering trajectory routes that represent the frequent movement behaviors of a user. In addition to spatial and ...
Exploring frequency-based approaches for efficient trajectory classification
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied ComputingIn the last few years, several trajectory classification methods have been proposed for mobility data collected from GPS devices. Most of them only use information derived from the physical movement of the object, as speed, acceleration, and direction ...
Private Trajectory Data Publication for Trajectory Classification
Web Information Systems and ApplicationsAbstractTrajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, ...
Comments