{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:46:39Z","timestamp":1772901999021,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176197"],"award-info":[{"award-number":["62176197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-024-10632-1","type":"journal-article","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T01:46:13Z","timestamp":1733535973000},"page":"22215-22228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Incorporating prior knowledge for domain generalization traffic flow anomaly detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Bo","family":"Chen","sequence":"first","affiliation":[]},{"given":"Min","family":"Fang","sequence":"additional","affiliation":[]},{"given":"HaoJie","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"10632_CR1","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Spec Lect IE 2:1\u201318","journal-title":"Spec Lect IE"},{"key":"10632_CR2","unstructured":"Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd international conference on learning representations, ICLR 2015"},{"key":"10632_CR3","doi-asserted-by":"crossref","unstructured":"Bashar MA, Nayak R (2020) Tanogan: time series anomaly detection with generative adversarial networks. In: 2020 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1778\u20131785","DOI":"10.1109\/SSCI47803.2020.9308512"},{"key":"10632_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0320-4","volume-title":"Time series: theory and methods","author":"PJ Brockwell","year":"1991","unstructured":"Brockwell PJ, Davis RA (1991) Time series: theory and methods. Springer, Berlin"},{"key":"10632_CR5","doi-asserted-by":"crossref","unstructured":"Carmona CU, Aubet FX, Flunkert V, Gasthaus J (2022) Neural contextual anomaly detection for time series. In: Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22, pp 2843\u20132851","DOI":"10.24963\/ijcai.2022\/394"},{"key":"10632_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/S0968-090X(97)00016-8","volume":"5","author":"H Dia","year":"1997","unstructured":"Dia H, Rose G (1997) Development and evaluation of neural network freeway incident detection models using field data. Transp Res Part C Emerg Technol 5:313\u2013331","journal-title":"Transp Res Part C Emerg Technol"},{"key":"10632_CR7","doi-asserted-by":"crossref","unstructured":"Geiger A, Liu D, Alnegheimish S, Cuesta-Infante A, Veeramachaneni K (2020) Tadgan: time series anomaly detection using generative adversarial networks. In: 2020 IEEE international conference on big data (Big Data), IEEE, pp 33\u201343","DOI":"10.1109\/BigData50022.2020.9378139"},{"key":"10632_CR8","doi-asserted-by":"crossref","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906), IEEE, pp 1735\u20131742","DOI":"10.1109\/CVPR.2006.100"},{"key":"10632_CR9","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.aap.2018.12.022","volume":"124","author":"M Hossain","year":"2019","unstructured":"Hossain M, Abdel-Aty M, Quddus MA, Muromachi Y, Sadeek SN (2019) Real-time crash prediction models: state-of-the-art, design pathways and ubiquitous requirements. Accid Anal Prev 124:66\u201384","journal-title":"Accid Anal Prev"},{"key":"10632_CR10","doi-asserted-by":"publisher","first-page":"105392","DOI":"10.1016\/j.aap.2019.105392","volume":"135","author":"T Huang","year":"2020","unstructured":"Huang T, Wang S, Sharma A (2020) Highway crash detection and risk estimation using deep learning. Accid Anal Prev 135:105392","journal-title":"Accid Anal Prev"},{"key":"10632_CR11","doi-asserted-by":"crossref","unstructured":"Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 387\u2013395","DOI":"10.1145\/3219819.3219845"},{"key":"10632_CR12","doi-asserted-by":"publisher","first-page":"103178","DOI":"10.1016\/j.trc.2021.103178","volume":"127","author":"K Kalair","year":"2021","unstructured":"Kalair K, Connaughton C (2021) Anomaly detection and classification in traffic flow data from fluctuations in the flow-density relationship. Transp Res Part C Emerg Technol 127:103178","journal-title":"Transp Res Part C Emerg Technol"},{"key":"10632_CR13","doi-asserted-by":"crossref","unstructured":"Kawachi Y, Koizumi Y, Harada N (2018) Complementary set variational autoencoder for supervised anomaly detection. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2366\u20132370","DOI":"10.1109\/ICASSP.2018.8462181"},{"key":"10632_CR14","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks"},{"key":"10632_CR15","doi-asserted-by":"crossref","unstructured":"Lai G, Chang WC, Yang Y, Liu H (2018) Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 95\u2013104","DOI":"10.1145\/3209978.3210006"},{"key":"10632_CR16","first-page":"1461","volume":"2016","author":"L Li","year":"2016","unstructured":"Li L, He S, Zhang J, Yang F (2016) Bagging-SVMs algorithm-based traffic incident detection. CICTP 2016:1461\u20131469","journal-title":"CICTP"},{"key":"10632_CR17","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1080\/23249935.2020.1813214","volume":"18","author":"L Li","year":"2022","unstructured":"Li L, Lin Y, Du B, Yang F, Ran B (2022) Real-time traffic incident detection based on a hybrid deep learning model. Transportmetrica A Transp Sci 18:78\u201398","journal-title":"Transportmetrica A Transp Sci"},{"key":"10632_CR18","doi-asserted-by":"crossref","unstructured":"Li L, Zhang J, Zheng Y, Ran B (2018) Real-time traffic incident detection with classification methods. In: Green intelligent transportation systems: proceedings of the 7th international conference on green intelligent transportation system and safety 7. Springer, pp 777\u2013788","DOI":"10.1007\/978-981-10-3551-7_62"},{"key":"10632_CR19","doi-asserted-by":"crossref","unstructured":"Li P, Li D, Li W, Gong S, Fu Y, Hospedales TM (2021) A simple feature augmentation for domain generalization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8886\u20138895","DOI":"10.1109\/ICCV48922.2021.00876"},{"key":"10632_CR20","doi-asserted-by":"publisher","first-page":"105628","DOI":"10.1016\/j.aap.2020.105628","volume":"144","author":"Y Lin","year":"2020","unstructured":"Lin Y, Li L, Jing H, Ran B, Sun D (2020) Automated traffic incident detection with a smaller dataset based on generative adversarial networks. Accid Anal Prev 144:105628","journal-title":"Accid Anal Prev"},{"key":"10632_CR21","first-page":"289","volume":"6","author":"Z Liu","year":"2006","unstructured":"Liu Z, Zhu M, Fan K (2006) One-class learning based algorithm for the freeway automatic incident detection. IJCSNS 6:289","journal-title":"IJCSNS"},{"key":"10632_CR22","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","volume":"22","author":"M Lv","year":"2020","unstructured":"Lv M, Hong Z, Chen L, Chen T, Zhu T, Ji S (2020) Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans Intell Transp Syst 22:3337\u20133348","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10632_CR23","first-page":"139","volume":"2","author":"LM Manevitz","year":"2001","unstructured":"Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2:139\u2013154","journal-title":"J Mach Learn Res"},{"key":"10632_CR24","unstructured":"Nippani A, Li D, Ju H, Koutsopoulos H, Zhang H (2024) Graph neural networks for road safety modeling: datasets and evaluations for accident analysis. In: Advances in neural information processing systems, vol 36"},{"key":"10632_CR25","unstructured":"Payne H, Helfenbein E, Knobel H (1976) Development and testing of incident detection algorithms, volume 2: Research methodology and detailed results. Technical Report"},{"key":"10632_CR26","unstructured":"Payne HJ, Tignor SC (1978) Freeway incident-detection algorithms based on decision trees with states. In: Transportation Research Record"},{"key":"10632_CR27","doi-asserted-by":"crossref","unstructured":"Pena EH, de\u00a0Assis MV, Proen\u00e7a ML (2013) Anomaly detection using forecasting methods arima and hwds. In: 2013 32nd international conference of the Chilean computer science society (sccc), IEEE. pp 63\u201366","DOI":"10.1109\/SCCC.2013.18"},{"key":"10632_CR28","unstructured":"Persaud BN, Hall FL, Hall LM. (1990) Congestion identification aspects of the McMaster incident detection algorithm. In: Transportation research record"},{"key":"10632_CR29","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TNSM.2020.3004415","volume":"17","author":"TV Phan","year":"2020","unstructured":"Phan TV, Nguyen TG, Dao NN, Huong TT, Thanh NH, Bauschert T (2020) Deepguard: efficient anomaly detection in SDN with fine-grained traffic flow monitoring. IEEE Trans Netw Service Manag 17:1349\u20131362","journal-title":"IEEE Trans Netw Service Manag"},{"key":"10632_CR30","unstructured":"Qu E, Wang Y, Luo X, He W, Ren K, Li D (2024) CNN kernels can be the best shapelets"},{"key":"10632_CR31","doi-asserted-by":"publisher","first-page":"106090","DOI":"10.1016\/j.aap.2021.106090","volume":"154","author":"MA Rahim","year":"2021","unstructured":"Rahim MA, Hassan HM (2021) A deep learning based traffic crash severity prediction framework. Accid Anal Prev 154:106090","journal-title":"Accid Anal Prev"},{"key":"10632_CR32","doi-asserted-by":"crossref","unstructured":"Ringberg H, Soule A, Rexford J, Diot C (2007) Sensitivity of PCA for traffic anomaly detection. In: Proceedings of the 2007 ACM SIGMETRICS international conference on measurement and modeling of computer systems, pp 109\u2013120","DOI":"10.1145\/1254882.1254895"},{"key":"10632_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3322695","author":"IT Sarteshnizi","year":"2023","unstructured":"Sarteshnizi IT, Bagloee SA, Sarvi M, Nassir N (2023) Traffic anomaly detection: exploiting temporal positioning of flow-density samples. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2023.3322695","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10632_CR34","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1109\/ACCESS.2020.3047340","volume":"9","author":"Q Shang","year":"2020","unstructured":"Shang Q, Feng L, Gao S (2020) A hybrid method for traffic incident detection using random forest-recursive feature elimination and long short-term memory network with bayesian optimization algorithm. IEEE Access 9:1219\u20131232","journal-title":"IEEE Access"},{"key":"10632_CR35","first-page":"30","volume":"35","author":"S Shanthi","year":"2011","unstructured":"Shanthi S, Ramani RG (2011) Classification of vehicle collision patterns in road accidents using data mining algorithms. Int J Comput Appl 35:30\u201337","journal-title":"Int J Comput Appl"},{"key":"10632_CR36","doi-asserted-by":"crossref","unstructured":"Song Y, Ning C, Fa L, Liu X, Shu X, Li Z, Tang J (2017) Multi-part boosting LSTMs for skeleton based human activity analysis. In: 2017 IEEE international conference on multimedia & expo workshops (ICMEW), IEEE. pp 605\u2013608","DOI":"10.1109\/ICMEW.2017.8026279"},{"key":"10632_CR37","first-page":"211","volume":"6","author":"I Steinwart","year":"2005","unstructured":"Steinwart I, Hush D, Scovel C (2005) A classification framework for anomaly detection. J Mach Learn Res 6:211\u2013232","journal-title":"J Mach Learn Res"},{"key":"10632_CR38","doi-asserted-by":"publisher","first-page":"106779","DOI":"10.1016\/j.aap.2022.106779","volume":"176","author":"Y Sun","year":"2022","unstructured":"Sun Y, Mallick T, Balaprakash P, Macfarlane J (2022) A data-centric weak supervised learning for highway traffic incident detection. Accid Anal Prev 176:106779","journal-title":"Accid Anal Prev"},{"key":"10632_CR39","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TITS.2004.843112","volume":"6","author":"S Tang","year":"2005","unstructured":"Tang S, Gao H (2005) Traffic-incident detection-algorithm based on nonparametric regression. IEEE Trans Intell Transp Syst 6:38\u201342","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10632_CR40","doi-asserted-by":"publisher","first-page":"104354","DOI":"10.1016\/j.trc.2023.104354","volume":"156","author":"T Tran","year":"2023","unstructured":"Tran T, He D, Kim J, Hickman M (2023) Msgnn: a multi-structured graph neural network model for real-time incident prediction in large traffic networks. Transp Res Part C Emerg Technol 156:104354","journal-title":"Transp Res Part C Emerg Technol"},{"key":"10632_CR41","doi-asserted-by":"crossref","unstructured":"Truong-Huu T, Dheenadhayalan N, Pratim\u00a0Kundu P, Ramnath V, Liao J, Teo SG, Praveen\u00a0Kadiyala S (2020) An empirical study on unsupervised network anomaly detection using generative adversarial networks. In: Proceedings of the 1st ACM workshop on security and privacy on artificial intelligence, pp 20\u201329","DOI":"10.1145\/3385003.3410924"},{"key":"10632_CR42","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1080\/23249935.2020.1711543","volume":"16","author":"W Wu","year":"2020","unstructured":"Wu W, Jiang S, Liu R, Jin W, Ma C (2020) Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model. Transportmetrica A Transport Sci 16:359\u2013387","journal-title":"Transportmetrica A Transport Sci"},{"key":"10632_CR43","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.neucom.2018.12.016","volume":"332","author":"B Yang","year":"2019","unstructured":"Yang B, Sun S, Li J, Lin X, Tian Y (2019) Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332:320\u2013327","journal-title":"Neurocomputing"},{"key":"10632_CR44","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Chen Y, Feng X, Lumezanu C, Cheng W, Ni J, Zong B, Chen H, Chawla NV (2019) A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI conference on artificial intelligence, pp 1409\u20131416","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"10632_CR45","doi-asserted-by":"crossref","unstructured":"Zhang Y, Dong X, Shang L, Zhang D, Wang D (2020) A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing. In: 2020 17th Annual IEEE international conference on sensing, communication, and networking (SECON), IEEE. pp 1\u20139","DOI":"10.1109\/SECON48991.2020.9158447"},{"key":"10632_CR46","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.eswa.2016.03.029","volume":"57","author":"D Zheng","year":"2016","unstructured":"Zheng D, Li F, Zhao T (2016) Self-adaptive statistical process control for anomaly detection in time series. Expert Syst Appl 57:324\u2013336","journal-title":"Expert Syst Appl"},{"key":"10632_CR47","doi-asserted-by":"publisher","first-page":"4874","DOI":"10.1007\/s10489-020-02041-3","volume":"51","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Ren H, Li Z, Wu N, Al-Ahmari AM (2021) Anomaly detection via a combination model in time series data. Appl Intell 51:4874\u20134887","journal-title":"Appl Intell"},{"key":"10632_CR48","first-page":"829","volume":"61","author":"H Zhu","year":"2019","unstructured":"Zhu H, Meng F, Rho S, Li M, Wang J, Liu S, Jiang F (2019) Long short term memory networks based anomaly detection for KPIs. Comput Mater Contin 61:829\u2013847","journal-title":"Comput Mater Contin"},{"key":"10632_CR49","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1108\/JICV-03-2021-0004","volume":"4","author":"W Zhu","year":"2021","unstructured":"Zhu W, Wu J, Fu T, Wang J, Zhang J, Shangguan Q (2021) Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP. J Intell Connect Veh 4:80\u201391","journal-title":"J Intell Connect Veh"},{"key":"10632_CR50","unstructured":"Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International conference on learning representations"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10632-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10632-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10632-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T01:49:36Z","timestamp":1757123376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10632-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":50,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10632"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10632-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"13 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}