{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:17:41Z","timestamp":1772039861556,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030352301","type":"print"},{"value":"9783030352318","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-35231-8_41","type":"book-chapter","created":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:30:38Z","timestamp":1573864238000},"page":"565-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spatial-Temporal Recurrent Neural Network for Anomalous Trajectories Detection"],"prefix":"10.1007","author":[{"given":"Yunyao","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Song","sequence":"additional","affiliation":[]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"issue":"1","key":"41_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/TVCG.2016.2598416","volume":"23","author":"S Al-Dohuki","year":"2016","unstructured":"Al-Dohuki, S.: SemanticTraj: a new approach to interacting with massive taxi trajectories. IEEE Trans. Vis. Comput. Graph. 23(1), 11\u201320 (2016)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"41_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint \narXiv:1409.0473\n\n (2014)"},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Bu, Y., Chen, L., Wai-Chee Fu, A., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 159\u2013168. ACM (2009)","DOI":"10.1145\/1557019.1557043"},{"key":"41_CR4","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-642-30973-1_6","volume-title":"Mobile and Ubiquitous Systems: Computing, Networking, and Services","author":"C Chen","year":"2012","unstructured":"Chen, C., Zhang, D., Samuel Castro, P., Li, N., Sun, L., Li, S.: Real-time detection of anomalous taxi trajectories from GPS traces. In: Puiatti, A., Gu, T. (eds.) MobiQuitous 2011. LNICST, vol. 104, pp. 63\u201374. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-30973-1_6"},{"issue":"2","key":"41_CR5","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1109\/TITS.2013.2238531","volume":"14","author":"C Chen","year":"2013","unstructured":"Chen, C., et al.: iBOAT: isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806\u2013818 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"41_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1007\/978-3-030-18579-4_45","volume-title":"Database Systems for Advanced Applications","author":"B Cheng","year":"2019","unstructured":"Cheng, B., et al.: STL: online detection of taxi trajectory anomaly based on spatial-temporal laws. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11447, pp. 764\u2013779. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-18579-4_45"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Ge, Y., Xiong, H., Liu, C., Zhou, Z.-H.: A taxi driving fraud detection system. In: 2011 IEEE 11th International Conference on Data Mining, pp. 181\u2013190. IEEE (2011)","DOI":"10.1109\/ICDM.2011.18"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Ge, Y., Xiong, H., Zhou, Z.-H., Ozdemir, H., Yu, J., Lee, K.C.: Top-eye: top-k evolving trajectory outlier detection. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1733\u20131736. ACM (2010)","DOI":"10.1145\/1871437.1871716"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645\u20136649. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Hallac, D., Bhooshan, S., Chen, M., Abida, K., Leskovec, J., et al.: Drive2vec: multiscale state-space embedding of vehicular sensor data. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3233\u20133238. IEEE (2018)","DOI":"10.1109\/ITSC.2018.8569550"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Lee, J.-G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering. IEEE, April 2008","DOI":"10.1109\/ICDE.2008.4497422"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Han, J., Kim, S., Gonzalez, H.: Roam: rule- and motif-based anomaly detection in massive moving object data sets. In: Proceedings of 7th SIAM International Conference on Data Mining (2007)","DOI":"10.1137\/1.9781611972771.25"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412\u20131421, Lisbon, Portugal. Association for Computational Linguistics, September 2015","DOI":"10.18653\/v1\/D15-1166"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: BMVC (2008)","DOI":"10.5244\/C.22.103"},{"key":"41_CR16","unstructured":"Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment TreeBank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631\u20131642 (2013)"},{"key":"41_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-05090-0_23","volume-title":"Advanced Data Mining and Applications","author":"L Song","year":"2018","unstructured":"Song, L., Wang, R., Xiao, D., Han, X., Cai, Y., Shi, C.: Anomalous trajectory detection using recurrent neural network. In: Gan, G., Li, B., Li, X., Wang, S. (eds.) ADMA 2018. LNCS (LNAI), vol. 11323, pp. 263\u2013277. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-030-05090-0_23"},{"key":"41_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/3-540-47724-1_2","volume-title":"Advances in Spatial and Temporal Databases","author":"M Vazirgiannis","year":"2001","unstructured":"Vazirgiannis, M., Wolfson, O.: A spatiotemporal model and language for moving objects on road networks. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 20\u201335. Springer, Heidelberg (2001). \nhttps:\/\/doi.org\/10.1007\/3-540-47724-1_2"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Wu, H., Sun, W., Zheng, B.: A fast trajectory outlier detection approach via driving behavior modeling. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 837\u2013846. ACM, New York (2017)","DOI":"10.1145\/3132847.3132933"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Yu, Y., Cao, L., Rundensteiner, E.A., Wang, Q.: Detecting moving object outliers in massive-scale trajectory streams. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 422\u2013431. ACM, New York (2014)","DOI":"10.1145\/2623330.2623735"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, D., et al.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99\u2013108. ACM (2011)","DOI":"10.1145\/2030112.2030127"},{"issue":"3","key":"41_CR22","first-page":"29","volume":"6","author":"Y Zheng","year":"2015","unstructured":"Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"41_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-319-26190-4_2","volume-title":"Web Information Systems Engineering \u2013 WISE 2015","author":"J Zhu","year":"2015","unstructured":"Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Time-dependent popular routes based trajectory outlier detection. WISE 2015. LNCS, vol. 9418, pp. 16\u201330. Springer, Cham (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-319-26190-4_2"},{"issue":"2","key":"41_CR24","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/1132956.1132959","volume":"38","author":"J Zobel","year":"2006","unstructured":"Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2), 6 (2006)","journal-title":"ACM Comput. Surv."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35231-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:44:36Z","timestamp":1573865076000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35231-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030352301","9783030352318"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35231-8_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"15 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dalian","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2019.neusoft.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}