Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
review-article

A survey on the computation of representative trajectories

Published: 02 April 2024 Publication History

Abstract

The process of computing a representative trajectory for a set of raw (or even semantically enriched) trajectories is an attractive solution to minimize several challenges related to trajectory management, like trajectory data integration or trajectory pattern analysis. We identify two main strategies for accomplishing such a process (trajectory data summarization and trajectory data fusion), but we argue that this subject is still an open issue, and we did not find a survey with such a focus. In order to fill this literature gap, this paper presents a survey that analyzes several issues around the two aforementioned strategies, like the type of representative data computed by each approach, the dimensions that are considered by the approach (spatial, temporal, and semantics), the accomplished methods of the proposed processes, and how the process is evaluated. Additionally, we compare these two research areas (trajectory summarization and trajectory fusion) in literature to analyze their relationship. Finally, some open issues related to this subject are also pointed out.

References

[1]
dos Santos Mello R, Bogorny V, Alvares LO, Santana LHZ, Ferrero CA, Frozza AA, Schreiner GA, and Renso C MASTER: A multiple aspect view on trajectories Trans GIS 2019 23 4 805-822
[2]
Richly K (2018) A survey on trajectory data management for hybrid transactional and analytical workloads. In: 2018 IEEE International conference on big data (Big Data), pp 562–569. IEEE, Seattle, United States
[3]
Su H, Liu S, Zheng B, Zhou X, and Zheng K A survey of trajectory distance measures and performance evaluation VLDB J 2020 29 1 3-32
[4]
Wang S, Bao Z, Culpepper JS, Cong G (2021) A survey on trajectory data management, analytics, and learning. ACM Comput Surv 54(2)
[5]
Feng Z and Zhu Y A survey on trajectory data mining: Techniques and applications IEEE Access 2016 4 2056-2067
[6]
Georgiou H, Karagiorgou S, Kontoulis Y, Pelekis N, Petrou P, Scarlatti D, Theodoridis Y (2018) Moving objects analytics: Survey on future location & trajectory prediction methods. arXiv: abs/1807.04639
[7]
Bian J, Tian D, Tang Y, Tao D (2018) A survey on trajectory clustering analysis. CoRR arXiv: 1802.06971
[8]
Leite da Silva C, May Petry L, Bogorny V (2019) A survey and comparison of trajectory classification methods. In: 2019 8th Brazilian conference on intelligent systems (BRACIS), pp 788–793. IEEE, Brazil
[9]
Fiore M, Katsikouli P, Zavou E, Cunche M, Fessant F, Hello DL, Aïvodji UM, Olivier B, Quertier T, Stanica R (2019) Privacy of trajectory micro-data : a survey. ArXiv: 1903.12211
[10]
Ahmed SA, Dogra DP, Kar S, and Roy PP Trajectory-based surveillance analysis: A survey IEEE Trans Circuits Syst Video Technol 2019 29 7 1985-1997
[11]
Esteban J, Starr A, Willetts R, Hannah P, and Bryanston-Cross P A review of data fusion models and architectures: towards engineering guidelines Neural Comput Appl 2005 14 4 273-281
[12]
Hall DL and Llinas J An introduction to multisensor data fusion Proc IEEE 1997 85 1 6-23
[13]
Doan A, Halevy A, and Ives Z Principles of Data Integration 2012 Burlington, United States Morgan Kaufmann
[14]
Zhao H and Ram S Combining schema and instance information for integrating heterogeneous data sources Data Knowl Eng 2007 61 2 281-303
[15]
Dong XL, Srivastava D (2015) Big Data Integration vol 7, pp 1–198. Morgan & Claypool Publishers, Williston, United States
[16]
Sazontev V (2018) Methods for big data integration in distributed computation environments. In: XX International conference on data analytics and management in data intensive domains (DAMDID/RCDL 2018), Moscow, Russia, pp 238–244
[17]
Ma B, Jiang T, Zhou X, Zhao F, and Yang Y A novel data integration framework based on unified concept model IEEE Access 2017 5 5713-5722
[18]
Taleb I, Serhani MA, Bouhaddioui C, and Dssouli R Big data quality framework: a holistic approach to continuous quality management J Big Data 2021 8 1 1-41
[19]
Hesabi ZR, Tari Z, Goscinski A, Fahad A, Khalil I, and Queiroz C Khan SU and Zomaya AY Data summarization techniques for big data–a survey Handbook on Data Centers 2015 New York, United States Springer 1109-1152
[20]
Chandola V and Kumar V Summarization-compressing data into an informative representation Knowl Inf Syst 2007 12 355-378
[21]
Ahmed M Data summarization: a survey Knowl Inf Syst 2019 58 2 249-273
[22]
Blelloch GE (2013) Introduction to data compression*. Computer Science Department, Carnegie Mellon University, 55
[23]
Desu MM A selection problem Ann Math Stat 1970 41 5 1596-1603
[24]
Nakamura EF, Loureiro AA, and Frery AC Information fusion for wireless sensor networks: Methods, models, and classifications ACM Comput Surv (CSUR) 2007 39 3 9
[25]
Daoui M, Lalam M, Hamrioui S, Djamah B, and Nouali D Circuit of data aggregation on the fly for wsn Sens Transd 2012 142 7 44
[26]
Amigo D, Sánchez Pedroche D, García J, and Molina JM Review and classification of trajectory summarisation algorithms: From compression to segmentation Int J Distrib Sens Netw 2021 17 10 15501477211050729
[27]
Martinez D, Cristobal S, Belkoura S (2018) Smart data fusion: Probabilistic record linkage adapted to merge two trajectories from different sources. Proceedings of the SESAR Innovation Days],(Dec 2018)
[28]
Gao C, Zhao Y, Wu R, Yang Q, and Shao J Semantic trajectory compression via multi-resolution synchronization-based clustering Knowl-Based Syst 2019 174 177-193
[29]
Lee J-G, Han J, Whang K-Y (2007) Trajectory clustering: A partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data. SIGMOD ’07, pp 593–604. Association for Computing Machinery (ACM), New York, United States
[30]
Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, and Theodoridis Y Segmentation and sampling of moving object trajectories based on representativeness IEEE Trans Knowl Data Eng 2012 24 7 1328-1343
[31]
Wang H, Su H, Zheng K, Sadiq S, Zhou X (2013) An effectiveness study on trajectory similarity measures. Proceedings of the twenty-fourth Australasian database conference 137, 13–22. Australian Computer Society, Inc
[32]
Buchin K, Buchin M, Van Kreveld M, Löffler M, Silveira RI, Wenk C, and Wiratma L Median trajectories Algorithmica 2013 66 3 595-614
[33]
Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd international conference on knowledge discovery and data mining. AAAIWS’94, pp 359–370. AAAI Press, Seattle, WA
[34]
Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering, pp 673–684. IEEE, San Jose, United States
[35]
Chen L, Özsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data. SIGMOD ’05, pp. 491–502. Association for Computing Machinery (ACM), Baltimore, Maryland
[36]
Peixoto DA (2018) A distributed in-memory database system for large-scale spatial-temporal trajectory data. PhD thesis, University of Queensland, Australia. Doctor of Philosophy - School of Information Technology and Electrical Engineering
[37]
Buchin M, Kilgus B, and Kölzsch A Group diagrams for representing trajectories Int J Geogr Inf Sci 2019 34 12 2401-2433
[38]
Eiter T, Mannila H (1994) Computing discrete frechet distance. Technical report cd-tr 94/64, Christian Doppler Laboratory for Expert Systems, TU Vienna - Austria
[39]
Ying X, Xu Z, Yin WG (2009) Cluster-based congestion outlier detection method on trajectory data. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, vol. 5, pp. 243–247. IEEE
[40]
Frentzos E, Gratsias K, Pelekis N, and Theodoridis Y Algorithms for nearest neighbor search on moving object trajectories Geoinformatica 2007 11 159-193
[41]
Furtado AS, Alvares LOC, Pelekis N, Theodoridis Y, and Bogorny V Unveiling movement uncertainty for robust trajectory similarity analysis Int J Geogr Inf Sci 2018 32 1 140-168
[42]
Furtado AS, Kopanaki D, Alvares LO, and Bogorny V Multidimensional similarity measuring for semantic trajectories Trans GIS 2016 20 2 280-298
[43]
Lehmann AL, Alvares LO, and Bogorny V SMSM: a similarity measure for trajectory stops and moves Int J Geogr Inf Sci 2019 33 9 1847-1872
[44]
Petry LM, Ferrero CA, Alvares LO, Renso C, and Bogorny V Towards semantic-aware multiple-aspect trajectory similarity measuring Trans GIS 2019 23 5 960-975
[45]
Xie P, Li T, Liu J, Du S, Yang X, and Zhang J Urban flow prediction from spatiotemporal data using machine learning: A survey Inf Fus 2020 59 1-12
[46]
de Almeida DR, de Souza Baptista C, de Andrade FG, and Soares A A survey on big data for trajectory analytics ISPRS Int J Geo-Information 2020 9 2 88
[47]
Kong X, Li M, Ma K, Tian K, Wang M, Ning Z, and Xia F Big trajectory data: A survey of applications and services IEEE Access 2018 6 58295-58306
[48]
Ayhan S, Samet H (2015) Diclerge: Divide-cluster-merge framework for clustering aircraft trajectories. In: Proceedings of the 8th ACM SIGSPATIAL international workshop on computational transportation science, pp 7–14
[49]
Etienne L, Devogele T, Buchin M, and McArdle G Trajectory box plot: A new pattern to summarize movements Int J Geogr Inf Sci 2016 30 5 835-853
[50]
Borkowski P The ship movement trajectory prediction algorithm using navigational data fusion Sensors 2017 17 6 1432
[51]
Agarwal PK, Fox K, Munagala K, Nath A, Pan J, Taylor E (2018) Subtrajectory clustering: Models and algorithms. In: Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems, pp 75–87
[52]
Seep J, Vahrenhold J (2019) Inferring semantically enriched representative trajectories. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on computing with multifaceted movement data. MOVE’19, pp 1–4. Association for Computing Machinery, New York, United States
[53]
Zheng C, Peng Q, Xu X (2020) Heterogenous multi-source fusion for ship trajectory complement and prediction with sequence modeling. In: 2020 IEEE Fifth international conference on data science in cyberspace (DSC), pp 15–21. IEEE
[54]
Rodriguez DF, Ortiz AE (2020) Detecting representative trajectories in moving objects databases from clusters. In: International conference on information technology & systems, pp 141–151. Springer
[55]
Li H (2021) Typical trajectory extraction method for ships based on ais data and trajectory clustering. In: 2021 2nd International conference on artificial intelligence and information systems, pp 1–8
[56]
Machado VL, Mello RdS, Bogorny V (2022) A method for summarizing trajectories with multiple aspects. In: International conference on database and expert systems applications, pp 433–446. Springer
[57]
Ruan S, Li R, Bao J, He T, Zheng Y (2018) Cloudtp: A cloud-based flexible trajectory preprocessing framework. In: 2018 IEEE 34th international conference on data engineering (ICDE), pp 1601–1604. IEEE
[58]
Lian J, Zhang L (2018) One-month beijing taxi gps trajectory dataset with taxi ids and vehicle status. In: Proceedings of the first workshop on data acquisition to analysis, pp 3–4
[59]
Yang D, Zhang D, Zheng VW, and Yu Z Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs IEEE Trans Syst Man Cybern: Syst 2015 45 1 129-142
[60]
Santipantakis GM, Glenis A, Patroumpas K, Vlachou A, Doulkeridis C, Vouros GA, Pelekis N, and Theodoridis Y Spartan: Semantic integration of big spatio-temporal data from streaming and archival sources Future Gener Comput Syst 2018 110 540-555

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Geoinformatica
Geoinformatica  Volume 28, Issue 4
Oct 2024
173 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 02 April 2024
Accepted: 19 March 2024
Revision received: 10 October 2023
Received: 28 October 2022

Author Tags

  1. Trajectory fusion
  2. Trajectory summarization
  3. Representative trajectory

Qualifiers

  • Review-article

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025

Other Metrics

Citations

View Options

View options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media