survey

Trajectory Data Mining: An Overview

Author:

Yu ZhengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 3

Article No.: 29, Pages 1 - 41

https://doi.org/10.1145/2743025

Published: 12 May 2015 Publication History

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.

References

[1]

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.

Digital Library

[2]

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.

Digital Library

[3]

R. Agrawal, C. Faloutsos, and A. Swami. 1993. Efficient similarity search in sequence databases. Springer, 69--84.

Digital Library

[4]

H. Alt, A. Efrat, G. Rote, and C. Wenk. 2003. Matching planar maps. Journal of Algorithms 49, 2 (2003), 262--283.

Digital Library

[5]

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.

Digital Library

[6]

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.

Digital Library

[7]

R. Bellman. 1961. On the approximation of curves by line segments using dynamic programming. Communications of the ACM 4, 6 (1961), 284.

Digital Library

[8]

A. R. Beresford and F. Stajano. 2003. Location privacy in pervasive computing. IEEE Pervasive Computing 2, 1 (2003), 46--55.

Digital Library

[9]

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.

Digital Library

[10]

T. Brinkhoff and O. Str, 2002. A framework for generating network-based moving objects. Geoinformatica, 6, 2 (2002), 153--180.

Digital Library

[11]

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.

Digital Library

[12]

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.

Digital Library

[13]

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.

Digital Library

[14]

V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly detection: A survey. ACM Computing Surveys 41, 3 (2009), 1--58.

Digital Library

[15]

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.

Digital Library

[16]

S. S. Chawathe. 2007. Segment-based map matching. 2007 IEEE Intelligent Vehicles Symposium. IEEE, 1190--1197.

[17]

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.

Digital Library

[18]

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.

Digital Library

[19]

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.

Digital Library

[20]

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.

Digital Library

[21]

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.

Digital Library

[22]

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.

[23]

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.

Digital Library

[24]

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).

Digital Library

[25]

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.

[26]

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.

Digital Library

[27]

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.

[28]

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.

[29]

C. Duntgen, T. Behr, and R. H. Guting. 2009. BerlinMOD: A benchmark for moving object databases. The VLDB Journal 18, 6 (2009), 1335--1368.

Digital Library

[30]

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.

Digital Library

[31]

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.

Digital Library

[32]

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.

Digital Library

[33]

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.

Digital Library

[34]

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.

Digital Library

[35]

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.

Digital Library

[36]

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.

[37]

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.

Digital Library

[38]

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.

Digital Library

[39]

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.

[40]

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.

Digital Library

[41]

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.

Digital Library

[42]

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.

Digital Library

[43]

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.

[44]

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.

Digital Library

[45]

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.

Digital Library

[46]

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.

Digital Library

[47]

A. Kharrat, I. S. Popa, K. Zeitouni, and S. Faiz. 2008. Clustering algorithm for network constraint trajectories. Headway in Spatial Data Handling. 631--647.

[48]

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.

[49]

J. Krumm. 2011. Trajectory analysis for driving. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 213--241.

[50]

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.

[51]

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.

Digital Library

[52]

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.

Digital Library

[53]

W.-C. Lee and J. Krumm. 2011. Trajectory preprocessing. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 1--31.

[54]

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.

Digital Library

[55]

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.

Digital Library

[56]

Z. Li, J. Lee, X. Li, and J. Han. 2010b. Incremental clustering for trajectories. Database Systems for Advanced Applications. 32--46.

Digital Library

[57]

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.

Digital Library

[58]

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.

Digital Library

[59]

L. Liao, D. Fox, and H. Kautz. 2004. Learning and inferring transportation routines. In Proceedings of the National Conference on Artificial Intelligence. 348--353.

Digital Library

[60]

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.

Digital Library

[61]

S. Liu, L. Ni, and R. Krishnan. 2014. Fraud detection from Taxis’ driving behaviors. IEEE Transactions on Vehicular Technology 63, 1 (2014), 464--472.

[62]

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.

Digital Library

[63]

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.

Digital Library

[64]

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.

Digital Library

[65]

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.

Digital Library

[66]

S. Ma, Y. Zheng, and O. Wolfson. 2015. Real-time city-scale taxi ridesharing. IEEE Transactions on Knowledge and Data Engineering 99.

[67]

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.

[68]

R. B. McMaster. 1986. A statistical analysis of mathematical measures of linear simplification. The American Cartographer 13, 2 (1986), 103--116.

[69]

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.

[70]

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.

[71]

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.

Digital Library

[72]

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.

Digital Library

[73]

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.

Digital Library

[74]

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.

Digital Library

[75]

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.

[76]

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.

Digital Library

[77]

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.

Digital Library

[78]

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.

Digital Library

[79]

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.

[80]

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.

Digital Library

[81]

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.

Digital Library

[82]

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.

Digital Library

[83]

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.

[84]

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.

Digital Library

[85]

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.

Digital Library

[86]

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.

[87]

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.

[88]

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.

[89]

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.

Digital Library

[90]

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.

Digital Library

[91]

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.

Digital Library

[92]

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.

Digital Library

[93]

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.

Digital Library

[94]

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.

Digital Library

[95]

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.

Digital Library

[96]

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.

Digital Library

[97]

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).

Digital Library

[98]

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.

Digital Library

[99]

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.

Digital Library

[100]

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.

Digital Library

[101]

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.

Digital Library

[102]

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.

Digital Library

[103]

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.

Digital Library

[104]

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.

Digital Library

[105]

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.

Digital Library

[106]

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.

Digital Library

[107]

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.

[108]

H. Xie, L. Kulik, and E. Tanin. 2010. Privacy-aware traffic monitoring. IEEE Transactions on Intelligent Transportation Systems 11, 1 (2010), 61--70.

Digital Library

[109]

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.

Digital Library

[110]

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.

[111]

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.

Digital Library

[112]

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.

[113]

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.

Digital Library

[114]

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.

Digital Library

[115]

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.

Digital Library

[116]

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.

Digital Library

[117]

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.

Digital Library

[118]

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.

Digital Library

[119]

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.

Digital Library

[120]

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.

Digital Library

[121]

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.

Digital Library

[122]

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.

Digital Library

[123]

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.

Digital Library

[124]

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.

Digital Library

[125]

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.

Digital Library

[126]

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.

Digital Library

[127]

J. Yuan, Y. Zheng, L. Zhang, X. Xie, and G. Sun. 2011b. Where to find my next passenger&quest; In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 109--118.

Digital Library

[128]

N. J. Yuan, Y. Zheng, and X. Xie. 2012. Segmentation of Urban Areas using Road Networks. Technical Report MSR-TR-2012-65.

[129]

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.

Digital Library

[130]

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.

[131]

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.

Digital Library

[132]

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.

Digital Library

[133]

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.

Digital Library

[134]

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.

Digital Library

[135]

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.

Digital Library

[136]

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.

[137]

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.

[138]

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.

Digital Library

[139]

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.

Digital Library

[140]

Y. Zheng. 2011. Location-based social networks: users. Computing with Spatial Trajectories, Y. Zheng and X. Zhou (Eds.). Springer, 243--276.

Digital Library

[141]

Y. Zheng. 2012. Tutorial on location-based social networks. In Proceedings of the 21st International Conference on World Wide Web. ACM.

[142]

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.

Digital Library

[143]

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.

[144]

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.

Digital Library

[145]

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.

Digital Library

[146]

Y. Zheng, S. E. Koonin, and O. E. Wolfson. 2013. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. ACM.

[147]

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.

Digital Library

[148]

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.

Digital Library

[149]

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.

Digital Library

[150]

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.

Digital Library

[151]

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.

Digital Library

[152]

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.

Digital Library

[153]

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.

[154]

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.

Digital Library

[155]

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.

Digital Library

[156]

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.

Digital Library

[157]

Y. Zheng and X. Zhou. 2011. Computing with Spatial Trajectories. Springer.

Digital Library

[158]

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.

[159]

GeoLife Data: http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx.

[160]

T-Drive Data: http://research.microsoft.com/apps/pubs/&quest;id=152883.

[161]

Trajectory with transportation modes: http://research.microsoft.com/apps/pubs/&quest;id=141896.

[162]

User check-in data: https://www.dropbox.com/s/4nwb7zpsj25ibyh/check-in&percnt;20data.zip.

[163]

Hurricane trajectory (HURDAT): http://www.nhc.noaa.gov/data/hurdat.

[164]

The Greek Trucks Dataset,” http://www.chorochronos.org.

[165]

Movebank data: https://www.movebank.org/.

Cited By

Chen YKuang KWu C(2024)Trajectory classification to support effective and efficient field-road classificationPeerJ Computer Science10.7717/peerj-cs.194510(e1945)Online publication date: 28-Mar-2024
https://doi.org/10.7717/peerj-cs.1945
Paulauskas VPaulauskas D(2024)Dependence of Ships Turning at Port Turning Basins on Clearance under the Ship’s KeelSustainability10.3390/su1607281916:7(2819)Online publication date: 28-Mar-2024
https://doi.org/10.3390/su16072819
Zhang LWang BZhang QZhu SMa Y(2024)Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal FeaturesSensors10.3390/s2415497124:15(4971)Online publication date: 31-Jul-2024
https://doi.org/10.3390/s24154971
Show More Cited By

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 Computing

In 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 Applications
Abstract
Trajectory 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

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 3

Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

May 2015

319 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2764959

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 May 2015

Accepted: 01 November 2014

Revised: 01 May 2014

Received: 01 October 2013

Published in TIST Volume 6, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Survey
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,152
Total Citations
View Citations
13,467
Total Downloads

Downloads (Last 12 months)1,189
Downloads (Last 6 weeks)106

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chen YKuang KWu C(2024)Trajectory classification to support effective and efficient field-road classificationPeerJ Computer Science10.7717/peerj-cs.194510(e1945)Online publication date: 28-Mar-2024
https://doi.org/10.7717/peerj-cs.1945
Paulauskas VPaulauskas D(2024)Dependence of Ships Turning at Port Turning Basins on Clearance under the Ship’s KeelSustainability10.3390/su1607281916:7(2819)Online publication date: 28-Mar-2024
https://doi.org/10.3390/su16072819
Zhang LWang BZhang QZhu SMa Y(2024)Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal FeaturesSensors10.3390/s2415497124:15(4971)Online publication date: 31-Jul-2024
https://doi.org/10.3390/s24154971
Bin Asad KYuan Y(2024)The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social MediaSensors10.3390/s2412379624:12(3796)Online publication date: 12-Jun-2024
https://doi.org/10.3390/s24123796
Shu YChen XDi X(2024)Mobility Pattern Analysis during Russia–Ukraine War Using Twitter Location DataInformation10.3390/info1502007615:2(76)Online publication date: 27-Jan-2024
https://doi.org/10.3390/info15020076
Yang YZuo XZhao KLi Y(2024)Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory DataISPRS International Journal of Geo-Information10.3390/ijgi1306019713:6(197)Online publication date: 12-Jun-2024
https://doi.org/10.3390/ijgi13060197
Tian RGao PLiu Y(2024)A privacy-preserving vehicle trajectory clustering framework隐私保护下的车辆轨迹聚类方法研究Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230036925:7(988-1002)Online publication date: 27-Jul-2024
https://doi.org/10.1631/FITEE.2300369
Martins GFerreira DCarneiro CNogueira-Paiva NBianchi A(2024)Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopyPLOS ONE10.1371/journal.pone.030471619:6(e0304716)Online publication date: 3-Jun-2024
https://doi.org/10.1371/journal.pone.0304716
Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Chen WHuang CYu YJiang YDong J(2024)Trajectory-User Linking via Hierarchical Spatio-Temporal Attention NetworksACM Transactions on Knowledge Discovery from Data10.1145/363571818:4(1-22)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3635718
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents