{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T03:23:12Z","timestamp":1789269792772,"version":"build-2803163510"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004204","name":"Tongji University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004204","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1601100"],"award-info":[{"award-number":["2018YFB1601100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis &amp; Prevention"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.aap.2022.106796","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T05:15:56Z","timestamp":1660626956000},"page":"106796","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":19,"special_numbering":"C","title":["A Bayesian deep learning method for freeway incident detection with uncertainty quantification"],"prefix":"10.1016","volume":"176","author":[{"given":"Genwang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaze","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianbiao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0005","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3141\/1897-12","article-title":"Predicting freeway crashes from loop detector data by matched case-control logistic regression","volume":"1897","author":"Abdel-Aty","year":"2004","journal-title":"Transp. Res. Rec."},{"issue":"5","key":"10.1016\/j.aap.2022.106796_b0010","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0968-090X(99)00022-4","article-title":"Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network","volume":"7","author":"Abdulhai","year":"1999","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"issue":"4","key":"10.1016\/j.aap.2022.106796_b0015","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1111\/0885-9507.00189","article-title":"An adaptive conjugate gradient neural network\u2013wavelet model for traffic incident detection","volume":"15","author":"Adeli","year":"2000","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"10.1016\/j.aap.2022.106796_b0020","doi-asserted-by":"crossref","first-page":"105973","DOI":"10.1016\/j.aap.2021.105973","article-title":"Traffic accident detection and condition analysis based on social networking data","volume":"151","author":"Ali","year":"2021","journal-title":"Accid. Anal. Prev."},{"issue":"7","key":"10.1016\/j.aap.2022.106796_b0025","doi-asserted-by":"crossref","first-page":"485","DOI":"10.3390\/ijgi10070485","article-title":"A3T-GCN: attention temporal graph convolutional network for traffic forecasting","volume":"10","author":"Bai","year":"2021","journal-title":"ISPRS Int. J. Geo-Inf."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0030","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/0361198196155400125","article-title":"Using probe-measured travel times to detect major freeway incidents in Houston Texas","volume":"1554","author":"Balke","year":"1996","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2022.106796_b0035","series-title":"Proceedings 15th World Congress on ITS","first-page":"5","article-title":"Using archived ITS data to automatically identify freeway bottlenecks in Portland, Oregon","author":"Bertini","year":"2008"},{"key":"10.1016\/j.aap.2022.106796_b0040","series-title":"Proceedings of International Conference on Machine Learning","first-page":"1613","article-title":"Weight uncertainty in neural network","author":"Blundell","year":"2015"},{"key":"10.1016\/j.aap.2022.106796_b0045","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.trc.2019.05.034","article-title":"Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds","volume":"105","author":"Chakraborty","year":"2019","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.aap.2022.106796_b0055","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.cogsys.2017.12.002","article-title":"Fuzzy deep learning based urban traffic incident detection","volume":"50","author":"El Hatri","year":"2018","journal-title":"Cognit. Syst. Res."},{"key":"10.1016\/j.aap.2022.106796_b0060","doi-asserted-by":"crossref","first-page":"106597","DOI":"10.1016\/j.asoc.2020.106597","article-title":"Uncertainty quantification for plant disease detection using Bayesian deep learning","volume":"96","author":"Hern\u00e1ndez","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.aap.2022.106796_b0065","first-page":"339","article-title":"Estimating the uncertainty of traffic forecasts from their historical accuracy","volume":"147","author":"Hoque","year":"2021","journal-title":"Transp. Res. Part A: Policy Pract."},{"key":"10.1016\/j.aap.2022.106796_b0070","series-title":"Proceedings of 86th Annual Meeting of the Transportation Research Board","first-page":"18","article-title":"Using PORTAL data to empirically diagnose freeway bottlenecks located on Oregon Highway 217","author":"Horowitz","year":"2007"},{"issue":"5","key":"10.1016\/j.aap.2022.106796_b0075","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1061\/(ASCE)0733-947X(1994)120:5(753)","article-title":"Application of fuzzy logic and neural networks to automatically detect freeway traffic incidents","volume":"120","author":"Hsiao","year":"1994","journal-title":"J. Transp. Eng."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2009.10.005","article-title":"A wavelet-based freeway incident detection algorithm with adapting threshold parameters","volume":"19","author":"Jeong","year":"2011","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.aap.2022.106796_b0085","doi-asserted-by":"crossref","first-page":"105520","DOI":"10.1016\/j.aap.2020.105520","article-title":"A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions","volume":"141","author":"Jiang","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2022.106796_b0150","series-title":"Multimodal Data Set Clean-up for Portland Oregon Metropolitan Region Data Set Description and Dictionary Freeway Data","year":"2012"},{"key":"10.1016\/j.aap.2022.106796_b0090","unstructured":"Kendall, A., Gal, Y., 2017. What uncertainties do we need in bayesian deep learning for computer vision? arXiv preprint arXiv:1703.04977."},{"issue":"2","key":"10.1016\/j.aap.2022.106796_b0095","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","article-title":"Aleatory or epistemic? Does it matter?","volume":"31","author":"Kiureghian","year":"2009","journal-title":"Struct. Saf."},{"key":"10.1016\/j.aap.2022.106796_b0100","unstructured":"LeCun, Y., 2015. LeNet-5, convolutional neural networks. URL:http:\/\/yann.lecun.com\/exdb\/lenet,20(5),14."},{"key":"10.1016\/j.aap.2022.106796_b0105","first-page":"52","article-title":"Incident detection: a Bayesian approach","volume":"682","author":"Levin","year":"1978","journal-title":"Transp. Res. Rec."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0110","doi-asserted-by":"crossref","first-page":"101","DOI":"10.3141\/2178-11","article-title":"Comparison of algorithms for systematic tracking of patterns of traffic congestion on freeways in Portland, Oregon","volume":"2178","author":"Li","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2022.106796_b0115","unstructured":"Li, B., Chen, C., Wang, W., Carin, L., 2018. Certified adversarial robustness with additive noise. arXiv preprint arXiv:1809.03113."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0120","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1080\/23249935.2020.1813214","article-title":"Real-time traffic incident detection based on a hybrid deep learning model","volume":"18","author":"Li","year":"2020","journal-title":"Transportmetrica A: Transp. Sci."},{"issue":"6","key":"10.1016\/j.aap.2022.106796_b0125","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1016\/j.trc.2011.05.014","article-title":"Incorporating uncertainty into short-term travel time predictions","volume":"19","author":"Li","year":"2011","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.aap.2022.106796_b0130","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.trc.2018.05.012","article-title":"Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development","volume":"92","author":"Lin","year":"2018","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.aap.2022.106796_b0135","doi-asserted-by":"crossref","first-page":"105628","DOI":"10.1016\/j.aap.2020.105628","article-title":"Automated traffic incident detection with a smaller dataset based on generative adversarial networks","volume":"144","author":"Lin","year":"2020","journal-title":"Accid. Anal. Prev."},{"issue":"8","key":"10.1016\/j.aap.2022.106796_b0140","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1049\/iet-its.2018.5317","article-title":"Dynamic traffic demand uncertainty prediction using radio-frequency identification data and link volume data","volume":"13","author":"Liu","year":"2019","journal-title":"IET Intel. Transport Syst."},{"key":"10.1016\/j.aap.2022.106796_b0145","doi-asserted-by":"crossref","first-page":"105703","DOI":"10.1016\/j.aap.2020.105703","article-title":"Automatic incident detection on freeways based on Bluetooth traffic monitoring","volume":"146","author":"Mercader","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2022.106796_b0155","unstructured":"Owens, N., Armstrong, A., Sullivan, P., Mitchell, C., Newton, D., Brewster, R., & Trego, T., 2010. Traffic incident management handbook. Federal Highway Administration, Office of Travel Management."},{"key":"10.1016\/j.aap.2022.106796_b0160","series-title":"Incident Management in Intelligent Transportation Systems","author":"Ozbay","year":"1999"},{"key":"10.1016\/j.aap.2022.106796_b0165","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.aap.2019.05.014","article-title":"Real-time accident detection: coping with imbalanced data","volume":"129","author":"Parsa","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2022.106796_b0170","first-page":"30","article-title":"Freeway incident-detection algorithms based on decision trees with states","volume":"682","author":"Payne","year":"1978","journal-title":"Transp. Res. Rec."},{"issue":"3","key":"10.1016\/j.aap.2022.106796_b0175","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1109\/TIE.2019.2907440","article-title":"Bayesian deep-learning-based health prognostics toward prognostics uncertainty","volume":"67","author":"Peng","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.aap.2022.106796_b0180","first-page":"167","article-title":"Congestion identification aspects of the McMaster incident detection algorithm","volume":"1287","author":"Persaud","year":"1990","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2022.106796_b0185","series-title":"Proceedings of Proceedings of the 86th Annual Meeting of the Transportation Research Board","first-page":"18","article-title":"Incorporating Incident Data into a Freeway Data Archive for Improved Performance Measurement","author":"Potter","year":"2007"},{"key":"10.1016\/j.aap.2022.106796_b0190","series-title":"Comparative Study of Threshold Selection for Incident Detection based on California Algorithm","first-page":"911","author":"Puangnak","year":"2019"},{"issue":"4","key":"10.1016\/j.aap.2022.106796_b0195","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1111\/0885-9507.00229","article-title":"Enhancing neural network traffic incident-detection algorithms using wavelets","volume":"16","author":"Samant","year":"2001","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"10.1016\/j.aap.2022.106796_b0200","series-title":"The 2007 Urban Mobility Report","author":"Schrank","year":"2007"},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0205","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TMI.2019.2919951","article-title":"Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT","volume":"39","author":"Seeb\u00f6ck","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.aap.2022.106796_b0210","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ins.2013.07.030","article-title":"Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty","volume":"255","author":"Senge","year":"2014","journal-title":"Inf. Sci."},{"key":"10.1016\/j.aap.2022.106796_b0215","unstructured":"Shridhar, K., Laumann, F., Liwicki, M., 2018. Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference. arXiv preprint arXiv:1806.05978."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0220","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1061\/(ASCE)0733-947X(1993)119:1(13)","article-title":"Application of filtering techniques for incident detection","volume":"119","author":"Stephanedes","year":"1993","journal-title":"J. Transp. Eng."},{"issue":"1","key":"10.1016\/j.aap.2022.106796_b0225","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TITS.2004.843112","article-title":"Traffic-incident detection-algorithm based on nonparametric regression","volume":"6","author":"Tang","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.aap.2022.106796_b0230","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1111\/mice.12010","article-title":"Fuzzy-entropy neural network freeway incident duration modeling with single and competing uncertainties","volume":"28","author":"Vlahogianni","year":"2013","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"issue":"12","key":"10.1016\/j.aap.2022.106796_b0235","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1109\/TITS.2016.2560769","article-title":"Multiple model particle filter for traffic estimation and incident detection","volume":"17","author":"Wang","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"10.1016\/j.aap.2022.106796_b0240","first-page":"1","article-title":"A survey on Bayesian deep learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"7","key":"10.1016\/j.aap.2022.106796_b0245","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1002\/atr.1231","article-title":"More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection","volume":"48","author":"Xiao","year":"2014","journal-title":"J. Adv. Transp."},{"key":"10.1016\/j.aap.2022.106796_b0250","series-title":"Proceedings of 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","first-page":"1","article-title":"Real-time Traffic Incident Detection Using an Autoencoder Model","author":"Yang","year":"2020"},{"key":"10.1016\/j.aap.2022.106796_b0255","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.trc.2014.08.001","article-title":"A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks","volume":"47","author":"Yuan","year":"2014","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"issue":"9","key":"10.1016\/j.aap.2022.106796_b0260","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-gcn: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2022.106796_b0265","series-title":"Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC)","first-page":"1011","article-title":"A deep learning approach for traffic incident detection in urban networks","author":"Zhu","year":"2018"}],"container-title":["Accident Analysis &amp; Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457522002317?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457522002317?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T23:30:17Z","timestamp":1757547017000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457522002317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":52,"alternative-id":["S0001457522002317"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2022.106796","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Bayesian deep learning method for freeway incident detection with uncertainty quantification","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2022.106796","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106796"}}