{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T08:10:54Z","timestamp":1770711054484,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2023R104"],"award-info":[{"award-number":["PNURSP2023R104"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The analysis of individuals\u2019 movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users\u2019 trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.<\/jats:p>","DOI":"10.3390\/ijgi12030117","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T01:35:37Z","timestamp":1678325737000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Efficient Trajectory Clustering with Road Network Constraints Based on Spatiotemporal Buffering"],"prefix":"10.3390","volume":"12","author":[{"given":"Syed Adil","family":"Hussain","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}]},{"given":"Muhammad Umair","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 \u00c5lesund, Norway"}]},{"given":"Wajeeha","family":"Nasar","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 \u00c5lesund, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-9163","authenticated-orcid":false,"given":"Sara","family":"Ghorashi","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}]},{"given":"Mona M.","family":"Jamjoom","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6763-2569","authenticated-orcid":false,"given":"Abdel-Haleem","family":"Abdel-Aty","sequence":"additional","affiliation":[{"name":"Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha 61922, Saudi Arabia"}]},{"given":"Amna","family":"Parveen","sequence":"additional","affiliation":[{"name":"College of Pharmacy, Gachon University, No. 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1252-260X","authenticated-orcid":false,"given":"Ibrahim A.","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 \u00c5lesund, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/01944363.2021.1957704","article-title":"The moving mapper: Participatory action research with big data","volume":"88","author":"Daepp","year":"2022","journal-title":"J. Am. Plan. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.1007\/s11277-021-09030-w","article-title":"Communication solutions for vehicle ad-hoc network in smart cities environment: A comprehensive survey","volume":"122","author":"Quy","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1016\/j.trpro.2021.02.145","article-title":"Big data in transport modelling and planning","volume":"54","author":"Iliashenko","year":"2021","journal-title":"Transp. Res. Procedia"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shu, W., and Li, Y. (2022). A novel demand-responsive customized bus based on improved ant colony optimization and clustering algorithms. IEEE Trans. Intell. Transp. Syst.","DOI":"10.1109\/TITS.2022.3145655"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10462-021-09994-y","article-title":"Spatiotemporal data mining: A survey on challenges and open problems","volume":"55","author":"Hamdi","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_6","first-page":"103115","article-title":"A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context","volume":"115","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10115-018-1186-x","article-title":"Analyzing large-scale human mobility data: A survey of machine learning methods and applications","volume":"58","author":"Toch","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1177\/03611981211058429","article-title":"Proposal for a pivot-based vehicle trajectory clustering method","volume":"2676","author":"Reyes","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_9","unstructured":"Paterson, M., and Dan\u010d\u00edk, V. Longest common subsequences. Proceedings of the International Symposium on Mathematical Foundations of Computer Science."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10044-011-0262-6","article-title":"A shape-based similarity measure for time series data with ensemble learning","volume":"16","author":"Nakamura","year":"2013","journal-title":"Pattern Anal. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1190\/1.1440410","article-title":"Machine contouring using minimum curvature","volume":"39","author":"Briggs","year":"1974","journal-title":"Geophysics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_13","first-page":"1","article-title":"Challenges, opportunities and barriers to sustainable transport development in functional urban areas","volume":"Volume 10","author":"Wolny","year":"2017","journal-title":"Environmental Engineering. Proceedings of the International Conference on Environmental Engineering"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1109\/TITS.2016.2547641","article-title":"Review and perspective for distance-based clustering of vehicle trajectories","volume":"17","author":"Besse","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11280-021-00903-5","article-title":"Semantic-aware heterogeneous information network embedding with incompatible meta-paths","volume":"25","author":"Zheng","year":"2022","journal-title":"World Wide Web"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1080\/13658816.2018.1432862","article-title":"Toward spacetime buffering for spatiotemporal proximity analysis of movement data","volume":"32","author":"Yuan","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-016-9477-7","article-title":"A review of moving object trajectory clustering algorithms","volume":"47","author":"Yuan","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_18","unstructured":"Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_19","unstructured":"Ding, C.H., He, X., Zha, H., Gu, M., and Simon, H.D. (December, January 29). A min-max cut algorithm for graph partitioning and data clustering. Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"58939","DOI":"10.1109\/ACCESS.2018.2866364","article-title":"Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cong, J., and Smith, M. (1993, January 14\u201318). A parallel bottom-up clustering algorithm with applications to circuit partitioning in VLSI design. Proceedings of the 30th International Design Automation Conference, Dallas, TX, USA.","DOI":"10.1145\/157485.165119"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.jedc.2008.12.007","article-title":"Integrated assessment of energy policies: Decomposing top-down and bottom-up","volume":"33","author":"Rutherford","year":"2009","journal-title":"J. Econ. Dyn. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0306-4379(01)00008-4","article-title":"Cure: An efficient clustering algorithm for large databases","volume":"26","author":"Guha","year":"2001","journal-title":"Inf. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_25","first-page":"427","article-title":"Survey on different grid based clustering algorithms","volume":"2","author":"Parikh","year":"2014","journal-title":"Int. J. Adv. Res. Comput. Sci. Manag. Stud."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.csda.2012.12.008","article-title":"Model-based clustering of high-dimensional data: A review","volume":"71","author":"Bouveyron","year":"2014","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_27","unstructured":"Zhang, Z., Huang, K., and Tan, T. (2006, January 20\u201324). Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., \u00d6zsu, M.T., and Oria, V. (2005, January 14\u201316). Robust and fast similarity search for moving object trajectories. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, MD, USA.","DOI":"10.1145\/1066157.1066213"},{"key":"ref_29","unstructured":"Eiter, T., and Mannila, H. (2022, December 02). Computing Discrete Fr\u00e9chet Distance 1994. Available online: https:\/\/www.researchgate.net\/publication\/228723178_Computing_Discrete_Frechet_Distance."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, R., Liu, L., and Song, J. (2011, January 9\u201311). Clustering of trajectories based on Hausdorff distance. Proceedings of the IEEE 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, China.","DOI":"10.1109\/ICECC.2011.6066483"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rick, C. (2000, January 5\u20137). Efficient computation of all longest common subsequences. Proceedings of the Scandinavian Workshop on Algorithm Theory, Bergen, Norway.","DOI":"10.1007\/3-540-44985-X_35"},{"key":"ref_32","unstructured":"Vlachos, M., Kollios, G., and Gunopulos, D. (March, January 26). Discovering similar multidimensional trajectories. Proceedings of the IEEE 18th International Conference on Data Engineering, San Jose, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1080\/13658816.2015.1104317","article-title":"Spatiotemporal data model for network time geographic analysis in the era of big data","volume":"30","author":"Chen","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1080\/13658816.2022.2128192","article-title":"A spatiotemporal data model and an index structure for computational time geography","volume":"37","author":"Chen","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/13658816.2013.816427","article-title":"Map-matching algorithm for large-scale low-frequency floating car data","volume":"28","author":"Chen","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/3\/117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:23Z","timestamp":1760122283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/3\/117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,8]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["ijgi12030117"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12030117","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,8]]}}}