{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:26:25Z","timestamp":1784305585654,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,12]],"date-time":"2018-01-12T00:00:00Z","timestamp":1515715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source\u2013destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.<\/jats:p>","DOI":"10.3390\/ijgi7010025","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T04:01:55Z","timestamp":1515988915000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8004-8367","authenticated-orcid":false,"given":"Yulong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Wang, Z. (2017). Correction: Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0171876"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3390\/ijgi2020371","article-title":"Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data","volume":"2","author":"Liu","year":"2013","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yin, P., Ye, M., Lee, W.C., and Li, Z. (2014). Mining GPS Data for Trajectory Recommendation. Advances in Knowledge Discovery and Data Mining, Springer International Publishing.","DOI":"10.1007\/978-3-319-06605-9_5"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Matsubara, Y., Li, L., Papalexakis, E., Lo, D., Sakurai, Y., and Faloutsos, C. (2013). F-Trail: Finding Patterns in Taxi Trajectories. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-642-37453-1_8"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/TVT.2013.2272792","article-title":"Fraud Detection from Taxis\u2019 Driving Behaviors","volume":"63","author":"Liu","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, Y., Yuan, J., and Xie, X. (2011). Urban computing with taxicabs. International Conference on Ubiquitous Computing, ACM.","DOI":"10.1145\/2030112.2030126"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1109\/TITS.2016.2614350","article-title":"A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports with a Mahalanobis Distance","volume":"18","author":"Li","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Q., Qiu, Q., Li, H., and Wu, Q. (2013, January 18\u201321). A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring. Proceedings of the IEEE International Conference on Computer-Aided Design, San Jose, CA, USA.","DOI":"10.1109\/ICCAD.2013.6691119"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1109\/TVCG.2013.228","article-title":"Visual Traffic Jam Analysis Based on Trajectory Data","volume":"19","author":"Wang","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., and Huang, Y. (2010, January 2\u20135). T-drive: Driving directions based on taxi trajectories. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869807"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ge, Y., Xiong, H., Liu, C., and Zhou, Z.H. (2011, January 11\u201314). A Taxi Driving Fraud Detection System. Proceedings of the 2011 IEEE 11th International Conference on Data Mining, Vancouver, BC, Canada.","DOI":"10.1109\/ICDM.2011.18"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, N., Zhou, Z.H., Chen, C., Sun, L., and Li, S. (2011, January 17\u201321). iBAT: Detecting anomalous taxi trajectories from GPS traces. Proceedings of the 13th international conference on Ubiquitous computing, Beijing, China.","DOI":"10.1145\/2030112.2030127"},{"key":"ref_13","first-page":"164","article-title":"Spatial and Temporal Characterization of Travel Patterns in a Traffic Network Using Vehicle Trajectories","volume":"9","author":"Kim","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1080\/13658816.2015.1063640","article-title":"Anomalous behavior detection in single-trajectory data","volume":"29","author":"Huang","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sakaki, T., Okazaki, M., and Matsuo, Y. (2010, January 26\u201330). Earthquake shakes Twitter users: Real-time event detection by social sensors. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772777"},{"key":"ref_16","first-page":"1","article-title":"A visual-numeric approach to clustering and anomaly detection for trajectory data","volume":"33","author":"Kumar","year":"2017","journal-title":"Vis. Comput. Int. J. Comput. Graph."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hwang, J.R., Kang, H.Y., and Li, K.J. (2005). Spatio-Temporal Similarity Analysis between Trajectories on Road Networks. Perspectives in Conceptual Modeling, Springer.","DOI":"10.1007\/11568346_30"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, J.G., Han, J., and Li, X. (2008, January 7\u201312). Trajectory Outlier Detection: A Partition-and-Detect Framework. Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, Cancun, Mexico.","DOI":"10.1109\/ICDE.2008.4497422"},{"key":"ref_20","first-page":"115","article-title":"Using Relative Distance and Hausdorff Distance to Mine Trajectory Clusters","volume":"11","author":"Guan","year":"2013","journal-title":"Telkomnika Indones. J. Electr. Eng."},{"key":"ref_21","unstructured":"Won, J.I., Kim, S.W., Baek, J.H., and Lee, J. (April, January 30). Trajectory clustering in road network environment. Proceedings of the CIDM IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, USA."},{"key":"ref_22","first-page":"361","article-title":"Searching for spatio-temporal similar trajectories on road networks using Network Voronoi Diagram","volume":"482","author":"Sha","year":"2015","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lee, J.G., Han, J., and Whang, K.Y. (2007, January 11\u201314). Trajectory clustering: A partition-and-group framework. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, China.","DOI":"10.1145\/1247480.1247546"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.trc.2011.12.008","article-title":"Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations","volume":"23","author":"Abraham","year":"2012","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","unstructured":"Chen, L., and Ng, R. (September, January 31). On The Marriage of Lp-norms and Edit Distance. Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, ON, Canada."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/13658816.2011.630003","article-title":"Movement similarity assessment using symbolic representation of trajectories","volume":"26","author":"Dodge","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1080\/13658816.2013.854369","article-title":"Measuring similarity of mobile phone user trajectories\u2013a Spatio-temporal Edit Distance method","volume":"28","author":"Yuan","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7573","DOI":"10.1016\/j.eswa.2015.06.014","article-title":"A general methodology for n-dimensional trajectory clustering","volume":"42","author":"Bermingham","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_30","unstructured":"Fu, Z., Hu, W., and Tan, T. (2005, January 14). Similarity based vehicle trajectory clustering and anomaly detection. Proceedings of the 2005 IEEE International Conference on Image Processing, Genova, Italy."},{"key":"ref_31","first-page":"47","article-title":"NNCluster: An efficient clustering algorithm for road network trajectories","volume":"Volume Part II","author":"Roh","year":"2010","journal-title":"Proceedings of the 15th International Conference on Database Systems for Advanced Applications"},{"key":"ref_32","unstructured":"Amorim, R.C. (2015). Feature Relevance in Ward\u2019s Hierarchical Clustering Using the Lp Norm, Springer."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G","article-title":"The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique","volume":"17","author":"Ketchen","year":"1996","journal-title":"Strateg. Manag. J."},{"key":"ref_34","unstructured":"Salvador, S., and Chan, P. (2004, January 15\u201317). Determining the Number of Clusters\/Segments in Hierarchical Clustering\/Segmentation Algorithms. Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.datak.2014.07.008","article-title":"WB-index: A sum-of-squares based index for cluster validity","volume":"92","author":"Zhao","year":"2014","journal-title":"Data Knowl. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/S0167-8655(97)00121-9","article-title":"Bayesian Ying\u2013Yang machine, clustering and number of clusters","volume":"18","author":"Xu","year":"1997","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","first-page":"1","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat."},{"key":"ref_38","first-page":"1101","article-title":"A trajectory clustering approach based on decision graph and data field for detecting hotspots","volume":"31","author":"Zhao","year":"2016","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wu, L., Hu, S., Yin, L., Wang, Y., Chen, Z., Guo, M., and Xie, Z. (2017). Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110373"},{"key":"ref_40","unstructured":"Buza, K.A. (2011). Fusion Methods for Time-Series Classification, Peter Lang."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Marussy, K., and Buza, K. (2013). SUCCESS: A New Approach for Semi-supervised Classification of Time-Series. Artificial Intelligence and Soft Computing, Springer.","DOI":"10.1007\/978-3-642-38658-9_39"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3125634","article-title":"Grid-Based Method for GPS Route Analysis for Retrieval","volume":"3","year":"2017","journal-title":"ACM Trans. Spat. Algorithms Syst."},{"key":"ref_43","unstructured":"Abello, J., Pardalos, P.M., and Resende, M.G.C. Clustering in Massive Data Sets. Handbook of Massive Data Sets, Springer."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/1\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:07Z","timestamp":1760194267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/1\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,12]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["ijgi7010025"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7010025","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,12]]}}}