survey

Vehicle Trajectory Similarity: Models, Methods, and Applications

Authors:

Roniel S. De Sousa,

Azzedine Boukerche,

Antonio A. F. LoureiroAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 5

Article No.: 94, Pages 1 - 32

https://doi.org/10.1145/3406096

Published: 28 September 2020 Publication History

Abstract

The increasing availability of vehicular trajectory data is at the core of smart mobility solutions. Such data offer us unprecedented information for the development of trajectory data mining-based applications. An essential task of trajectory analysis is the employment of efficient and accurate methods to compare trajectories. This work presents a systematic survey of vehicular trajectory similarity measures and provides a panorama of the research field. First, we show an overview of vehicle trajectory data, including the models and some preprocessing techniques. Then, we give a comprehensive review of methods to compare trajectories and their intrinsic properties. We classify the methods according to the trajectory representation and features such as metricity, computational complexity, and robustness to noise and local time shift. Last, we discuss the applications of vehicular trajectory similarity measures and some open research problems.

References

[1]

Sajimon Abraham and P. Sojan Lal. 2008. Trigger-based security alarming scheme for moving objects on road networks. In Intelligence and Security Informatics, Christopher C. Yang, Hsinchun Chen, Michael Chau, Kuiyu Chang, Sheau-Dong Lang, Patrick S. Chen, Raymond Hsieh, Daniel Zeng, Fei-Yue Wang, Kathleen Carley, Wenji Mao, and Justin Zhan (Eds.). Springer, Berlin, 92--101.

[2]

Sajimon Abraham and P. Sojan Lal. 2012. Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations. Transportation Research Part C: Emerging Technologies 23 (2012), 109--123. Data Management in Vehicular Networks.

[3]

Helmut Alt and Michael Godau. 1995. Computing the Fréchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 5, 01--02 (1995), 75--91.

[4]

Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. 1999. OPTICS: Ordering points to identify the clustering structure. SIGMOD Rec. 28, 2 (June 1999), 49--60.

Digital Library

[5]

Kendall Atkinson. 2002. Modelling a road using spline interpolation. Reports on Computational Mathematics 145 (2002), 1--17.

[6]

Ricardo A. Baeza-Yates, Walter Cunto, Udi Manber, and Sun Wu. 1994. Proximity matching using fixed-queries trees. In Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching (CPM’94). Springer-Verlag, Berlin, Heidelberg, 198--212. Retrieved from http://dl.acm.org/citation.cfm?id=647814.738307.

Digital Library

[7]

Ya Ban, Rui Wang, Hongjing Liu, Jing Yuan, Hao Luo, Fuli Yang, Ling Yu, and Xinping Xu. 2018. A moving objects index method integrating GeoHash and quadtree. In Proceedings of the International Conference on Computer Modeling, Simulation and Algorithm (CMSA’18). Atlantis Press, Paris, France, 4.

[8]

Gustavo E. Batista, Eamonn J. Keogh, Oben Moses Tataw, and Vinícius M. Souza. 2014. CID: An efficient complexity-invariant distance for time series. Data Min. Knowl. Discov. 28, 3 (May 2014), 634--669.

Digital Library

[9]

L. Bedogni, M. Fiore, and C. Glacet. 2018. Temporal reachability in vehicular networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18). IEEE Computer Society, Washington, DC, 81--89.

[10]

Clara Benevolo, Renata Paola Dameri, and Beatrice D’Auria. 2016. Smart mobility in smart city. In Empowering Organizations, Teresina Torre, Alessio Maria Braccini, and Riccardo Spinelli (Eds.). Springer International Publishing, Cham, 13--28.

[11]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (Aug. 2013), 1798--1828.

Digital Library

[12]

P. C. Besse, B. Guillouet, J. Loubes, and F. Royer. 2016. Review and perspective for distance-based clustering of vehicle trajectories. IEEE Trans. Intell. Transport. Syst. 17, 11 (Nov. 2016), 3306--3317.

Digital Library

[13]

Jiang Bian, Dayong Tian, Yuanyan Tang, and Dacheng Tao. 2018. A survey on trajectory clustering analysis. CoRR abs/1802.06971 (2018). arXiv:1802.06971 http://arxiv.org/abs/1802.06971

[14]

Tolga Bozkaya and Meral Ozsoyoglu. 1997. Distance-based indexing for high-dimensional metric spaces. SIGMOD Rec. 26, 2 (June 1997), 357--368.

Digital Library

[15]

C. Celes, F. A. Silva, A. Boukerche, R. M. d. C. Andrade, and A. A. F. Loureiro. 2017. Improving VANET simulation with calibrated vehicular mobility traces. IEEE Trans. Mobile Comput. 16, 12 (Dec. 2017), 3376--3389.

[16]

B. Chapuis and B. Garbinato. 2018. Geodabs: Trajectory indexing meets fingerprinting at scale. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS’18). IEEE Computer Society, Washington, DC, 1086--1095.

[17]

Edgar Chávez, Gonzalo Navarro, Ricardo Baeza-Yates, and José Luis Marroquín. 2001. Searching in metric spaces. ACM Comput. Surv. 33, 3 (Sept. 2001), 273--321.

Digital Library

[18]

Lei Chen. 2005. Similarity Search over Time Series and Trajectory Data. Ph.D. Dissertation. University of Waterloo, Waterloo, Ontario, Canada.

[19]

L. Chen, Y. Gao, X. Li, C. S. Jensen, and G. Chen. 2015. Efficient metric indexing for similarity search. In Proceedings of the IEEE 31st International Conference on Data Engineering. IEEE Computer Society, Washington, DC, 591--602.

[20]

Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, and Keyu Yang. 2017. Pivot-based metric indexing. Proc. VLDB Endow. 10, 10 (June 2017), 1058--1069.

Digital Library

[21]

Lei Chen and Raymond Ng. 2004. On the marriage of Lp-norms and edit distance. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB’04). VLDB Endowment, 792--803. Retrieved from http://dl.acm.org/citation.cfm?id=1316689.1316758.

[22]

Lei Chen, M. Tamer Özsu, and Vincent Oria. 2005. Robust and fast similarity search for moving object trajectories. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’05). ACM, New York, NY, 491--502.

Digital Library

[23]

Zaiben Chen, Heng Tao Shen, Xiaofang Zhou, Yu Zheng, and Xing Xie. 2010. Searching trajectories by locations: An efficiency study. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’10). ACM, New York, NY, 255--266.

Digital Library

[24]

Roniel S. de Sousa, Azzedine Boukerche, and Antonio A.F. Loureiro. 2020. A distributed and low-overhead traffic congestion control protocol for vehicular ad hoc networks. Comput. Commun. 159 (2020), 258--270.

[25]

R. S. de Sousa, A. Boukerche, and A. A. F. Loureiro. 2019. DisTraC: A distributed and low-overhead protocol for traffic congestion control using vehicular networks. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC). IEEE Computer Society, Washington, DC, 1--6.

[26]

R. S. de Sousa, F. S. da Costa, A. C. B. Soares, L. F. M. Vieira, and A. A. F. Loureiro. 2018. Geo-SDVN: A geocast protocol for software defined vehicular networks. In Proceedings of the IEEE International Conference on Communications (ICC). IEEE Computer Society, Washington, DC, 1--6.

[27]

Ke Deng, Kexin Xie, Kevin Zheng, and Xiaofang Zhou. 2011. Trajectory Indexing and Retrieval. Springer, New York, NY, 35--60.

[28]

Ze Deng, Yangyang Hu, Mao Zhu, Xiaohui Huang, and Bo Du. 2015. A scalable and fast OPTICS for clustering trajectory big data. Cluster Comput. 18, 2 (June 2015), 549--562.

Digital Library

[29]

John M. Dow, R. E. Neilan, and C. Rizos. 2009. The international GNSS service in a changing landscape of global navigation satellite systems. J. Geodesy 83, 3 (Mar. 2009), 191--198.

[30]

Thomas Eiter and Heikki Mannila. 1994. Computing Discrete Fréchet Distance. Technical Report. Citeseer.

[31]

T. Emrich, H. Kriegel, N. Mamoulis, M. Renz, and A. Zufle. 2012. Querying uncertain spatio-temporal data. In Proceedings of the IEEE 28th International Conference on Data Engineering. IEEE Computer Society, Washington, DC, 354--365.

[32]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, 226--231. http://dl.acm.org/citation.cfm?id=3001460.3001507

Digital Library

[33]

M. Maurice Fréchet. 1906. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo (1884--1940) 22, 1 (Dec. 1906), 1--72.

[34]

E. Frentzos, K. Gratsias, and Y. Theodoridis. 2007. Index-based most similar trajectory search. In Proceedings of the IEEE 23rd International Conference on Data Engineering. IEEE Computer Society, Washington, DC, 816--825.

[35]

Tao-Yang Fu and Wang-Chien Lee. 2020. Trembr: Exploring road networks for trajectory representation learning. ACM Trans. Intell. Syst. Technol. 11, 1, Article 10 (Feb. 2020), 25 pages.

Digital Library

[36]

Peter D. Grnwald, In Jae Myung, and Mark A. Pitt. 2005. Advances in Minimum Description Length: Theory and Applications (Neural Information Processing). MIT Press, Cambridge, MA.

[37]

Ralf Hartmut Güting, Michael H. Böhlen, Martin Erwig, Christian S. Jensen, Nikos A. Lorentzos, Markus Schneider, and Michalis Vazirgiannis. 2000. A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25, 1 (Mar. 2000), 1--42.

Digital Library

[38]

Ralf Hartmut Güting, Teixeira de Almeida, and Zhiming Ding. 2006. Modeling and querying moving objects in networks. VLDB J. 15, 2 (June 2006), 165--190.

Digital Library

[39]

Antonin Guttman. 1984. R-trees: A dynamic index structure for spatial searching. SIGMOD Rec. 14, 2 (June 1984), 47--57.

Digital Library

[40]

B. Han, L. Liu, and E. Omiecinski. 2015. Road-network aware trajectory clustering: Integrating locality, flow, and density. IEEE Trans. Mobile Comput. 14, 2 (Feb. 2015), 416--429.

[41]

P. Hao, K. Boriboonsomsin, G. Wu, and M. J. Barth. 2017. Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data. IEEE Trans. Intell. Transport. Syst. 18, 3 (Mar. 2017), 701--711.

Digital Library

[42]

David Hilbert. 1935. Über die stetige Abbildung einer Linie auf ein Flächenstück. Springer, Berlin, 1--2.

[43]

Kathleen Hornsby and Max J. Egenhofer. 2002. Modeling moving objects over multiple granularities. Ann. Math. Artific. Intell. 36, 1 (Sep. 2002), 177--194.

[44]

Weiming Hu, Xi Li, Guodong Tian, Stephen Maybank, and Zhongfei Zhang. 2013. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35, 5 (May 2013), 1051--1065.

[45]

Jung-Rae Hwang, Hye-Young Kang, and Ki-Joune Li. 2005. Spatio-temporal similarity analysis between trajectories on road networks. In Proceedings of the 24th International Conference on Perspectives in Conceptual Modeling (ER’05). Springer-Verlag, Berlin, 280--289.

Digital Library

[46]

Jung-Rae Hwang, Hye-Young Kang, and Ki-Joune Li. 2006. Searching for similar trajectories on road networks using spatio-temporal similarity. In Proceedings of the 10th East European Conference on Advances in Databases and Information Systems (ADBIS’06). Springer-Verlag, Berlin, Heidelberg, 282--295.

Digital Library

[47]

Anil K. Jain. 2010. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 8 (2010), 651--666.

Digital Library

[48]

H. Jiang, L. Chang, Q. Li, and D. Chen. 2019. Trajectory prediction of vehicles based on deep learning. In Proceedings of the 4th International Conference on Intelligent Transportation Engineering (ICITE’19). IEEE Computer Society, Washington, DC, 190--195.

[49]

M. Jiang and T. Zhao. 2019. Vehicle travel time estimation by sparse trajectories. In Proceedings of the IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC’19), Vol. 1. IEEE Computer Society, Washington, DC, 433--442.

[50]

Maurice G. Kendall. 1948. Rank Correlation Methods. Griffin, London.

[51]

Eamonn Keogh and Chotirat Ann Ratanamahatana. 2005. Exact indexing of dynamic time warping. Knowl. Info. Syst. 7, 3 (Mar. 2005), 358--386.

[52]

Eamonn J. Keogh and Michael J. Pazzani. 2000. Scaling up dynamic time warping for datamining applications. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’00). ACM, New York, NY, 285--289.

[53]

Jiwon Kim and Hani S. Mahmassani. 2015. Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Transport. Res. Procedia 9 (2015), 164--184.

[54]

Edwin M. Knorr, Raymond T. Ng, and Vladimir Tucakov. 2000. Distance-based outliers: Algorithms and applications. VLDB J. 8, 3 (Feb. 2000), 237--253.

Digital Library

[55]

M. Kubicka, A. Cela, H. Mounier, and S. Niculescu. 2018. Comparative study and application-oriented classification of vehicular map-matching methods. IEEE Intell. Transport. Syst. Mag. 10, 2 (2018), 150--166.

[56]

D. Kumar, M. Palaniswami, S. Rajasegarar, C. Leckie, J. C. Bezdek, and T. C. Havens. 2013. clusiVAT: A mixed visual/numerical clustering algorithm for big data. In Proceedings of the IEEE International Conference on Big Data. IEEE Computer Society, Washington, DC, 112--117.

[57]

Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, X. Wang, and C. Leckie. 2015. A scalable framework for clustering vehicle trajectories in a dense road network. In Proceedings of the ACM SIGKDD International Workshop Urban Computing. ACM, New York, NY.

[58]

D. Kumar, H. Wu, S. Rajasegarar, C. Leckie, S. Krishnaswamy, and M. Palaniswami. 2018. Fast and scalable big data trajectory clustering for understanding urban mobility. IEEE Trans. Intell. Transport. Syst. 19, 11 (Nov 2018), 3709--3722.

[59]

Jae-Gil Lee, Jiawei Han, and Xiaolei Li. 2008. Trajectory outlier detection: A partition-and-detect framework. In Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE’08). IEEE Computer Society, Washington, DC, 140--149.

Digital Library

[60]

Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’07). ACM, New York, NY, 593--604.

Digital Library

[61]

SangYoon Lee, Sanghyun Park, Woo-Cheol Kim, and Dongwon Lee. 2007. An efficient location encoding method for moving objects using hierarchical administrative district and road network. Info. Sci. 177, 3 (Feb. 2007), 832--843.

[62]

Stéphanie Lefèvre, Dizan Vasquez, and Christian Laugier. 2014. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J. 1, 1 (2014), 1.

[63]

Xiucheng Li, Gao Cong, Aixin Sun, and Yun Cheng. 2019. Learning travel time distributions with deep generative model. In The World Wide Web Conference (WWW’19). Association for Computing Machinery, New York, NY, 1017--1027.

Digital Library

[64]

X. Li, K. Zhao, G. Cong, C. S. Jensen, and W. Wei. 2018. Deep representation learning for trajectory similarity computation. In Proceedings of the IEEE 34th International Conference on Data Engineering (ICDE’18). IEEE Computer Society, Washington, DC, 617--628.

[65]

Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, and Yan Liu. 2018. Multi-task representation learning for travel time estimation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). Association for Computing Machinery, New York, NY, 1695--1704.

Digital Library

[66]

Siyuan Liu, Ce Liu, Qiong Luo, Lionel M. Ni, and Ramayya Krishnan. 2012. Calibrating large scale vehicle trajectory data. In Proceedings of the IEEE 13th International Conference on Mobile Data Management (Mdm 2012) (MDM’12). IEEE Computer Society, Washington, DC, 222--231.

Digital Library

[67]

Qiang Lu, Rencai Wang, Bin Yang, and Zhiguang Wang. 2019. Trajectory splicing. Knowl. Info. Syst. 62 (2019), 1--34.

[68]

J. Lv, Q. Li, Q. Sun, and X. Wang. 2018. T-CONV: A convolutional neural network for multi-scale taxi trajectory prediction. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp’18). IEEE Computer Society, Washington, DC, 82--89.

[69]

N. Magdy, M. A. Sakr, T. Mostafa, and K. El-Bahnasy. 2015. Review on trajectory similarity measures. In Proceedings of the IEEE 7th International Conference on Intelligent Computing and Information Systems (ICICIS’15). IEEE, Cairo, Egypt, 613--619.

[70]

Yingchi Mao, Haishi Zhong, Xianjian Xiao, and Xiaofang Li. 2017. A segment-based trajectory similarity measure in the urban transportation systems. Sensors 17, 3 (2017), 14.

[71]

Pierre-François Marteau. 2009. Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2 (Feb. 2009), 306--318.

Digital Library

[72]

Marina Meilă. 2006. The uniqueness of a good optimum for K-means. In Proceedings of the 23rd International Conference on Machine Learning (ICML’06). ACM, New York, NY, 625--632.

Digital Library

[73]

Fanrong Meng, Guan Yuan, Shaoqian Lv, Zhixiao Wang, and Shixiong Xia. 2018. An overview on trajectory outlier detection. Artific. Intell. Rev. (Feb. 2018), 7.

[74]

Luisa Micó, Jose Oncina, and Rafael C. Carrasco. 1996. A fast branch 8 bound nearest neighbour classifier in metric spaces. Pattern Recogn. Lett. 17, 7 (June 1996), 731--739.

Digital Library

[75]

Vaishali Mirge, Kesari Verma, and Shubhrata Gupta. 2017. Outlier detection in vehicle trajectories. Int. J. Comput. Appl. 171 (Aug. 2017), 1--6.

[76]

Boris Mirkin. 2016. Clustering: A Data Recovery Approach. Chapman and Hall/CRC, Boca Raton, FL.

[77]

Mirco Nanni and Dino Pedreschi. 2006. Time-focused clustering of trajectories of moving objects. J. Intell. Info. Syst. 27, 3 (Nov. 2006), 267--289.

Digital Library

[78]

Gonzalo Navarro. 2001. A guided tour to approximate string matching. ACM Comput. Surv. 33, 1 (Mar. 2001), 31--88.

Digital Library

[79]

Johannes Niedermayer, Andreas Züfle, Tobias Emrich, Matthias Renz, Nikos Mamoulis, Lei Chen, and Hans-Peter Kriegel. 2013. Probabilistic nearest neighbor queries on uncertain moving object trajectories. Proc. VLDB Endow. 7, 3 (Nov. 2013), 205--216.

Digital Library

[80]

Gustavo Niemeyer. 2008. Geohash. Retrieved from https://en.wikipedia.org/wiki/Geohash.

[81]

David Novak, Michal Batko, and Pavel Zezula. 2011. Metric index: An efficient and scalable solution for precise and approximate similarity search. Info. Syst. 36, 4 (2011), 721--733.

Digital Library

[82]

K. Okamoto, K. Berntorp, and S. Di Cairano. 2017. Similarity-based vehicle-motion prediction. In Proceedings of the American Control Conference (ACC’17). IEEE Computer Society, Washington, DC, 303--308.

[83]

Mostofa Ali Patwary, Diana Palsetia, Ankit Agrawal, Wei-keng Liao, Fredrik Manne, and Alok Choudhary. 2013. Scalable parallel OPTICS data clustering using graph algorithmic techniques. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC’13). ACM, New York, NY, Article 49, 12 pages.

Digital Library

[84]

Nikos Pelekis, Ioannis Kopanakis, Gerasimos Marketos, Irene Ntoutsi, Gennady Andrienko, and Yannis Theodoridis. 2007. Similarity search in trajectory databases. In Proceedings of the 14th International Symposium on Temporal Representation and Reasoning (TIME’07). IEEE Computer Society, Washington, DC, 129--140.

Digital Library

[85]

Dieter Pfoser and Christian S. Jensen. 1999. Capturing the uncertainty of moving-object representations. In Proceedings of the 6th International Symposium on Advances in Spatial Databases (SSD’99). Springer-Verlag, London, UK, 111--132. Retrieved from http://dl.acm.org/citation.cfm?id=647226.719082.

[86]

Dieter Pfoser and Christian S. Jensen. 2003. Indexing of network constrained moving objects. In Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems (GIS’03). ACM, New York, NY, 25--32.

[87]

M. Purss, R. Gibb, F. Samavati, P. Peterson, J. Rogers, J. Ben, and C. Dow. 2017. Topic 21: Discrete Global Grid Systems Abstract Specification. Technical Report. Open Geospatial Consortium, Wayland, MA.

[88]

Shaojie Qiao, Changjie Tang, Huidong Jin, Teng Long, Shucheng Dai, Yungchang Ku, and Michael Chau. 2010. PutMode: Prediction of uncertain trajectories in moving objects databases. Appl. Intell. 33, 3 (Dec. 2010), 370--386.

Digital Library

[89]

Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2012. Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM, New York, NY, 262--270.

Digital Library

[90]

S. Ranu, Deepak P, A. D. Telang, P. Deshpande, and S. Raghavan. 2015. Indexing and matching trajectories under inconsistent sampling rates. In Proceedings of the IEEE 31st International Conference on Data Engineering. IEEE Computer Society, Washington, DC, 999--1010.

[91]

Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, and James C. Bezdek. 2018. A scalable framework for trajectory prediction. Retrieved from http://arxiv.org/abs/1806.03582.

[92]

Joseph Lee Rodgers and W. Alan Nicewander. 1988. Thirteen ways to look at the correlation coefficient. Amer. Stat. 42, 1 (1988), 59--66. arXiv:https://doi.org/10.1080/00031305.1988.10475524

[93]

E. V. Ruiz. 1986. An algorithm for finding nearest neighbours in (approximately) constant average time. Pattern Recogn. Lett. 4, 3 (July 1986), 145--157.

[94]

Guillermo Ruiz, Francisco Santoyo, Edgar Chávez, Karina Figueroa, and Eric Sadit Tellez. 2013. Extreme pivots for faster metric indexes. In Similarity Search and Applications, Nieves Brisaboa, Oscar Pedreira, and Pavel Zezula (Eds.). Springer, Berlin, 115--126.

[95]

Stan Salvador and Philip Chan. 2007. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11, 5 (Oct. 2007), 561--580. Retrieved from http://dl.acm.org/citation.cfm?id=1367985.1367993.

Digital Library

[96]

I. Sanchez, Z. M. M. Aye, B. I. P. Rubinstein, and K. Ramamohanarao. 2016. Fast trajectory clustering using Hashing methods. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’16). IEEE Computer Society, Washington, DC, 3689--3696.

[97]

Swaminathan Sankararaman, Pankaj K. Agarwal, Thomas Mølhave, Jiangwei Pan, and Arnold P. Boedihardjo. 2013. Model-driven matching and segmentation of trajectories. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’13). ACM, New York, NY, 234--243.

[98]

Saul Schleimer, Daniel S. Wilkerson, and Alex Aiken. 2003. Winnowing: Local algorithms for document fingerprinting. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’03). ACM, New York, NY, 76--85.

Digital Library

[99]

Shuo Shang, Lisi Chen, Zhewei Wei, Christian S. Jensen, Kai Zheng, and Panos Kalnis. 2018. Parallel trajectory similarity joins in spatial networks. VLDB J. 27, 3 (June 2018), 395--420.

Digital Library

[100]

Tomás Skopal, Jaroslav Pokornỳ, and Vaclav Snasel. 2004. PM-tree: Pivoting metric tree for similarity search in multimedia databases. In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS’04). Springer-Verlag, Berlin, 16.

[101]

Han Su, Kai Zheng, Jiamin Huang, Haozhou Wang, and Xiaofang Zhou. 2015. Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J. 24, 1 (Feb. 2015), 93--116.

Digital Library

[102]

Na Ta, Guoliang Li, Yongqing Xie, Changqi Li, Shuang Hao, and Jianhua Feng. 2017. Signature-based trajectory similarity join. IEEE Trans. Knowl. Data Eng. 29, 4 (Apr. 2017), 870--883.

Digital Library

[103]

Pang-Ning Tan. 2018. Introduction to Data Mining. Pearson Education India.

[104]

E. Tiakas, A. N. Papadopoulos, A. Nanopoulos, Y. Manolopoulos, Dragan Stojanovic, and Slobodanka Djordjevic-Kajan. 2009. Searching for similar trajectories in spatial networks. J. Syst. Softw. 82, 5 (May 2009), 772--788.

Digital Library

[105]

Dalia Tiesyte and Christian S. Jensen. 2008. Similarity-based prediction of travel times for vehicles traveling on known routes. In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS’08). ACM, New York, NY, Article 14, 10 pages.

[106]

Kevin Toohey and Matt Duckham. 2015. Trajectory similarity measures. SIGSPATIAL Spec. 7, 1 (May 2015), 43--50.

Digital Library

[107]

Caetano Traina, Jr., Roberto F. Filho, Agma J. Traina, Marcos R. Vieira, Christos Faloutsos, and Christos Faloutsos. 2007. The omni-family of all-purpose access methods: A simple and effective way to make similarity search more efficient. VLDB J. 16, 4 (Oct. 2007), 483--505.

Digital Library

[108]

Goce Trajcevski. 2011. Uncertainty in Spatial Trajectories. Springer, New York, NY, 63--107.

[109]

Goce Trajcevski, Alok Choudhary, Ouri Wolfson, Li Ye, and Gang Li. 2010. Uncertain range queries for necklaces. In Proceedings of the 11th International Conference on Mobile Data Management (MDM’10). IEEE Computer Society, Washington, DC, 199--208.

Digital Library

[110]

Goce Trajcevski, Ouri Wolfson, Klaus Hinrichs, and Sam Chamberlain. 2004. Managing uncertainty in moving objects databases. ACM Trans. Database Syst. 29, 3 (Sept. 2004), 463--507.

Digital Library

[111]

Michail Vlachos, Dimitrios Gunopoulos, and George Kollios. 2002. Discovering similar multidimensional trajectories. In Proceedings of the 18th International Conference on Data Engineering (ICDE’02). IEEE Computer Society, Washington, DC, 673. Retrieved from http://dl.acm.org/citation.cfm?id=876875.878994.

Digital Library

[112]

Hua Wang, Changlong Gu, and Washington Yotto Ochieng. 2019. Vehicle trajectory reconstruction for signalized intersections with low-frequency floating car data. J. Adv. Transport. 2019 (2019), 14.

[113]

Haozhou Wang, Han Su, Kai Zheng, Shazia Sadiq, and Xiaofang Zhou. 2013. An effectiveness study on trajectory similarity measures. In Proceedings of the 24th Australasian Database Conference - Volume 137 (ADC’13). Australian Computer Society, Darlinghurst, Australia, 13--22. Retrieved from http://dl.acm.org/citation.cfm?id=2525416.2525418.

[114]

J. Won, S. Kim, J. Baek, and J. Lee. 2009. Trajectory clustering in road network environment. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining. IEEE Computer Society, Nashville, TN, 299--305.

[115]

Hao Wu, Weiwei Sun, and Baihua Zheng. 2017. A fast trajectory outlier detection approach via driving behavior modeling. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM’17). ACM, New York, NY, 837--846.

Digital Library

[116]

M. Wu, X. Zeng, Y. Lin, and Y. Wen. 2019. Vehicle trajectory prediction models by combining communication data. In Proceedings of the 11th International Conference on Advanced Computational Intelligence (ICACI’19). IEEE Computer Society, Washington, DC, 113--117.

[117]

Ying Xia, Guoyin Wang, Xu Zhang, Gyoung Bae Kim, and Hae-Young Bae. 2011. Spatio-temporal similarity measure for network constrained trajectory data. Int. J. Comput. Intell. Syst. 4 (2011), 1070--1079.

[118]

D. Yao, G. Cong, C. Zhang, and J. Bi. 2019. Computing trajectory similarity in linear time: A generic seed-guided neural metric learning approach. In Proceedings of the IEEE 35th International Conference on Data Engineering (ICDE’19). IEEE Computer Society, Washington, DC, 1358--1369.

[119]

D. Yao, C. Zhang, Z. Zhu, J. Huang, and J. Bi. 2017. Trajectory clustering via deep representation learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’17). IEEE Computer Society, Washington, DC, 3880--3887.

[120]

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2018. Time series joins, motifs, discords and shapelets: A unifying view that exploits the matrix profile. Data Min. Knowl. Discov. 32, 1 (Jan. 2018), 83--123.

[121]

Peter N. Yianilos. 1993. Data structures and algorithms for nearest neighbor search in general metric spaces. In Proceedings of the 4th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’93). Society for Industrial and Applied Mathematics, Philadelphia, PA, 311--321. Retrieved from http://dl.acm.org/citation.cfm?id=313559.313789.

[122]

Ge Yu, Yu Gu, Jianzhong Qiao, Lei Chen, and Chuanfei Xu. 2013. Interval reverse nearest neighbor queries on uncertain data with markov correlations. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’13). IEEE Computer Society, Washington, DC, 170--181.

[123]

Guan Yuan, Penghui Sun, Jie Zhao, Daxing Li, and Canwei Wang. 2017. A review of moving object trajectory clustering algorithms. Artif. Intell. Rev. 47, 1 (Jan. 2017), 123--144.

Digital Library

[124]

H. Yuan and G. Li. 2019. Distributed in-memory trajectory similarity search and join on road network. In Proceedings of the IEEE 35th International Conference on Data Engineering (ICDE’19). IEEE Computer Society, Washington, DC, 1262--1273.

[125]

Yu Zheng. 2015. Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. 6, 3, Article 29 (May 2015), 41 pages.

Digital Library

Cited By

Lei XWang H(2025)Non-Convex Metric Learning-Based Trajectory Clustering AlgorithmMathematics10.3390/math1303038713:3(387)Online publication date: 24-Jan-2025
https://doi.org/10.3390/math13030387
Fang JFeng CChao PXu JZhao PZhao L(2025)MDTRL: A Multi-Source Deep Trajectory Representation Learning for the Accurate and Fast Similarity QueryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351053426:2(2115-2128)Online publication date: Feb-2025
https://doi.org/10.1109/TITS.2024.3510534
Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
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Index Terms

Vehicle Trajectory Similarity: Models, Methods, and Applications
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 5

September 2021

782 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3426973

Editor:
Albert Zomaya
University of Sydney, Austraila

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Copyright © 2020 ACM.

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Publication History

Published: 28 September 2020

Accepted: 01 June 2020

Revised: 01 February 2020

Received: 01 September 2019

Published in CSUR Volume 53, Issue 5

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Cited By

Lei XWang H(2025)Non-Convex Metric Learning-Based Trajectory Clustering AlgorithmMathematics10.3390/math1303038713:3(387)Online publication date: 24-Jan-2025
https://doi.org/10.3390/math13030387
Fang JFeng CChao PXu JZhao PZhao L(2025)MDTRL: A Multi-Source Deep Trajectory Representation Learning for the Accurate and Fast Similarity QueryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351053426:2(2115-2128)Online publication date: Feb-2025
https://doi.org/10.1109/TITS.2024.3510534
Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
Kim MKwak BHou JKim T(2024)Robust Long-Term Vehicle Trajectory Prediction Using Link Projection and a Situation-Aware TransformerSensors10.3390/s2408239824:8(2398)Online publication date: 9-Apr-2024
https://doi.org/10.3390/s24082398
Li YWang Z(2024)biSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP TechniquesJournal of Marine Science and Engineering10.3390/jmse1206086812:6(868)Online publication date: 23-May-2024
https://doi.org/10.3390/jmse12060868
Schestakov SGottschalk SFunke TDemidova E(2024)RE-Trace: Re-identification of Modified GPS TrajectoriesACM Transactions on Spatial Algorithms and Systems10.1145/364368010:4(1-28)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3643680
Liu JLi JQiao LLi MStefanakis EZhao XHuang QWang HZhang C(2024) QuadGridSIM : A quadrilateral grid‐based method for high‐performance and robust trajectory similarity analysis Transactions in GIS10.1111/tgis.1312628:1(83-107)Online publication date: 4-Jan-2024
https://doi.org/10.1111/tgis.13126
Mou LXie HMao SYan DMa NYin BGao W(2024)Image-Based Structured Vehicle Behavior Analysis Inspired by Interactive CognitionIEEE Transactions on Multimedia10.1109/TMM.2024.338602626(9121-9134)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3386026
Hu DChen LFang HFang ZLi TGao Y(2024)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3323535(1-21)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3323535
Gao YFang ZXu JGong SShen CChen L(2024)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312319(1-17)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312319
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