{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:31:07Z","timestamp":1778545867184,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T00:00:00Z","timestamp":1513555200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702010"],"award-info":[{"award-number":["61702010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672039"],"award-info":[{"award-number":["61672039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program for University Top Talents of Anhui Province","award":["gxbjZD2016011"],"award-info":[{"award-number":["gxbjZD2016011"]}]},{"name":"Natural Science Foundation of Anhui Province (CN)","award":["1508085QF134"],"award-info":[{"award-number":["1508085QF134"]}]},{"name":"University Natural Science Research Program of Anhui Province","award":["KJ2017A327"],"award-info":[{"award-number":["KJ2017A327"]}]},{"name":"Science and Technology Project of Wuhu City","award":["2016cxy04"],"award-info":[{"award-number":["2016cxy04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1007\/s10489-017-1104-z","type":"journal-article","created":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T04:40:05Z","timestamp":1513572005000},"page":"2661-2680","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Trajectory outlier detection approach based on common slices sub-sequence"],"prefix":"10.1007","volume":"48","author":[{"given":"Qingying","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,18]]},"reference":[{"key":"1104_CR1","doi-asserted-by":"crossref","unstructured":"Lee JG, Han J, Li X (2008) Trajectory outlier detection: a partition-and-detect framework. In: Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE), pp 140\u2013149","DOI":"10.1109\/ICDE.2008.4497422"},{"issue":"1","key":"1104_CR2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00778-014-0365-y","volume":"24","author":"H Su","year":"2015","unstructured":"Su H, Zheng K, Huang J et al (2015) Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J 24(1):93\u2013116","journal-title":"VLDB J"},{"key":"1104_CR3","doi-asserted-by":"crossref","unstructured":"Sanchez I, Aye ZMM, Rubinstein BIP et al (2016) Fast trajectory clustering using hashing methods. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pp 3689\u20133696","DOI":"10.1109\/IJCNN.2016.7727674"},{"issue":"10","key":"1104_CR4","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TSMC.2014.2316742","volume":"44","author":"C Anagnostopoulos","year":"2014","unstructured":"Anagnostopoulos C, Hadjiefthymiades S (2014) Intelligent trajectory classification for improved movement prediction. IEEE Trans Syst Man Cybern Syst 44(10):1301\u20131314","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"17","key":"1104_CR5","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1049\/el.2012.1755","volume":"48","author":"CM Chen","year":"2012","unstructured":"Chen CM, Pi DC, Fang ZR (2012) Artificial immune algorithm applied to short-term prediction for mobile object location. Electron Lett 48(17):1061\u20131062","journal-title":"Electron Lett"},{"issue":"1","key":"1104_CR6","first-page":"1","volume":"25","author":"M Gupta","year":"2014","unstructured":"Gupta M, Gao J, Aggarwal CC (2014) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 25(1):1\u201320","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"1104_CR7","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/TPAMI.2013.172","volume":"36","author":"R Laxhammar","year":"2014","unstructured":"Laxhammar R, Falkman G (2014) Online learning and sequential anomaly detection in trajectories. IEEE Trans Pattern Anal Mach Intell 36(6):1158\u20131173","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1104_CR8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ins.2014.09.037","volume":"294","author":"M Shen","year":"2015","unstructured":"Shen M, Liu DR, Shann SH (2015) Outlier detection from vehicle trajectories to discover roaming events. Inform Sci 294:242\u2013254","journal-title":"Inform Sci"},{"issue":"1-2","key":"1104_CR9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10472-013-9381-7","volume":"74","author":"R Laxhammar","year":"2015","unstructured":"Laxhammar R, Falkman G (2015) Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories. Ann Math Artif Intell 74(1-2):67\u201394","journal-title":"Ann Math Artif Intell"},{"key":"1104_CR10","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2013","unstructured":"Han J, Kamber M, Pei J (2013) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco"},{"key":"1104_CR11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2017.03.008","volume":"90","author":"G Gan","year":"2017","unstructured":"Gan G, Ng M K -P (2017) K-means clustering with outlier removal. Pattern Recogn Lett 90:8\u201314","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"1104_CR12","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/TKDE.2012.234","volume":"26","author":"A Albanese","year":"2014","unstructured":"Albanese A, Pal S K (2014) Rough sets, kernel set, and spatiotemporal outlier detection. IEEE Trans Knowl Data Eng 26(1):194\u2013207","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1104_CR13","doi-asserted-by":"crossref","unstructured":"Aggarwal CC (2017) Outlier analysis, 2nd edn. Springer International Publishing","DOI":"10.1007\/978-3-319-47578-3"},{"key":"1104_CR14","doi-asserted-by":"crossref","unstructured":"Li Z, Ding B, Han J et al (2010) Swarm: mining relaxed temporal moving object clusters. In: Proceedings of the VLDB Endowment vol 3, no 1, pp 723\u2013734","DOI":"10.14778\/1920841.1920934"},{"key":"1104_CR15","unstructured":"Ge Y, Xiong H, Liu C et al (2012) A taxi driving fraud detection system. In: Proceedings of the IEEE International conference on data mining (ICDM), pp 181\u2013190"},{"key":"1104_CR16","doi-asserted-by":"crossref","unstructured":"Yu Y, Cao L, Rundensteiner EA et al (2014) Detecting moving object outliers in massive-scale trajectory streams. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, vol 8, pp 422\u2013431","DOI":"10.1145\/2623330.2623735"},{"issue":"3","key":"1104_CR17","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1080\/23754931.2016.1149874","volume":"2","author":"J Chen","year":"2016","unstructured":"Chen J, Abbady S, Duggimpudi MB (2016) Spatiotemporal outlier detection: did buoys tell where the hurricanes were?. Papers Appl Geograph 2(3):298\u2013314","journal-title":"Papers Appl Geograph"},{"issue":"3-4","key":"1104_CR18","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"EM Knorr","year":"2000","unstructured":"Knorr EM, Ng RT, Tucakov V (2000) Distance-based outliers: algorithms and applications. The VLDB J 8(3-4):237\u2013253","journal-title":"The VLDB J"},{"key":"1104_CR19","doi-asserted-by":"crossref","unstructured":"Li X (2007) ROAM: rule- and motif-based anomaly detection in massive moving object data sets. In: Proceedings of the SIAM international conference on data mining, pp 273\u2013284","DOI":"10.1137\/1.9781611972771.25"},{"key":"1104_CR20","doi-asserted-by":"crossref","unstructured":"Bu Y, Chen L, Fu A W -C et al (2009) Efficient anomaly monitoring over moving object trajectory streams. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 09), pp 159\u2013168","DOI":"10.1145\/1557019.1557043"},{"key":"1104_CR21","doi-asserted-by":"crossref","unstructured":"Zhu J, Jiang W, Liu A et al (2015) Time-dependent popular routes based trajectory outlier detection. In: Proceedings of the international conference on web information systems engineering, pp 16\u201330","DOI":"10.1007\/978-3-319-26190-4_2"},{"key":"1104_CR22","first-page":"907","volume-title":"An improving algorithm of trajectory outliers detection. Advances in intelligent and soft computing","author":"B Guan","year":"2012","unstructured":"Guan B, Zhang Y, Liu L et al (2012) An improving algorithm of trajectory outliers detection. Advances in intelligent and soft computing. Springer, Berlin, pp 907\u2013914"},{"key":"1104_CR23","doi-asserted-by":"crossref","unstructured":"Masciari E (2011) Trajectory outlier detection using an analytical approach. In: Proceedings of the 23rd IEEE international conference on tools with artificial intelligence, pp 377\u2013384","DOI":"10.1109\/ICTAI.2011.62"},{"key":"1104_CR24","doi-asserted-by":"crossref","unstructured":"Zhang D, Li N, Zhou Z-H et al (2011) iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th ACM international conference on Ubiquitous Computing (UbiComp 11), pp 99\u2013108","DOI":"10.1145\/2030112.2030127"},{"key":"1104_CR25","doi-asserted-by":"crossref","unstructured":"Mohamad I, Ali MAM, Ismail M (2011) Abnormal driving detection using real time global positioning system data. In: Proceedings of the IEEE international conference on space science and communication, pp 1\u20136","DOI":"10.1109\/IConSpace.2011.6015840"},{"issue":"2","key":"1104_CR26","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TITS.2013.2238531","volume":"14","author":"C Chen","year":"2013","unstructured":"Chen C, Zhang D, Castro PS et al (2013) iBOAT: isolation-based online anomalous trajectory detection. IEEE Trans Intell Transp Syst 14(2):806\u2013818","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1104_CR27","doi-asserted-by":"crossref","unstructured":"Li X, Li Z, Han J et al (2009) Temporal outlier detection in vehicle traffic data. In: Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp 1319\u20131322","DOI":"10.1109\/ICDE.2009.230"},{"key":"1104_CR28","doi-asserted-by":"crossref","unstructured":"Ge Y, Xiong H, Zhou Z et al (2010) TOP-EYE: top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM International conference on information and knowledge management, pp 1733\u20131736","DOI":"10.1145\/1871437.1871716"},{"issue":"1","key":"1104_CR29","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11280-016-0400-6","volume":"20","author":"J Zhu","year":"2017","unstructured":"Zhu J, Jiang W, Liu A et al (2017) Effective and efficient trajectory outlier detection based on time-dependent popular route. World Wide Web 20(1):111\u2013134","journal-title":"World Wide Web"},{"key":"1104_CR30","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ins.2012.04.015","volume":"208","author":"J Domingo-Ferrer","year":"2012","unstructured":"Domingo-Ferrer J, Trujillo-Rasua R (2012) Microaggregation- and permutation-based anonymization of movement data. Inform Sci 208:55\u201380","journal-title":"Inform Sci"},{"key":"1104_CR31","unstructured":"UNISYS (2015) Atlantic tropical storm tracking by year[EB\/OL]. \n                        http:\/\/weather.unisys.com\/hurricane\/atlantic\/"},{"key":"1104_CR32","doi-asserted-by":"publisher","unstructured":"Piorkowski M, Sarafijanovic-Djukic N, Grossglauser M (2009) CRAWDAD dataset epfl\/mobility(v 2009-02-24)[EB\/OL]. \n                        https:\/\/doi.org\/10.15783\/C7J010","DOI":"10.15783\/C7J010"},{"key":"1104_CR33","doi-asserted-by":"crossref","unstructured":"Piorkowski M, Sarafijanovic-Djukic N, Grossglauser M (2009) A parsimonious model of mobile partitioned networks with clustering. In: Proceedings of the 1st international conference on communication systems and NETworks, pp 1\u201310","DOI":"10.1109\/COMSNETS.2009.4808865"},{"issue":"12","key":"1104_CR34","doi-asserted-by":"crossref","first-page":"8745","DOI":"10.1016\/j.eswa.2010.06.040","volume":"37","author":"Y Chen","year":"2010","unstructured":"Chen Y, Miao D, Zhang H (2010) Neighborhood outlier detection. Expert Syst Appl 37(12):8745\u20138749","journal-title":"Expert Syst Appl"},{"key":"1104_CR35","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1016\/j.neucom.2015.08.071","volume":"173","author":"M Lv","year":"2016","unstructured":"Lv M, Chen L, Xu Z et al (2016) The discovery of personally semantic places based on trajectory data mining. Neurocomputing 173:1142\u20131153","journal-title":"Neurocomputing"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-017-1104-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-017-1104-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-017-1104-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T04:24:34Z","timestamp":1534220674000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-017-1104-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,18]]},"references-count":35,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,9]]}},"alternative-id":["1104"],"URL":"https:\/\/doi.org\/10.1007\/s10489-017-1104-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,18]]}}}