{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T08:48:02Z","timestamp":1782463682045,"version":"3.54.5"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis & Prevention"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aap.2026.108607","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T06:46:56Z","timestamp":1780296416000},"page":"108607","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Real-time lane-level abnormal traffic detection on freeways using sparse telematics data"],"prefix":"10.1016","volume":"234","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9190-9898","authenticated-orcid":false,"given":"Shixiao","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6337-0450","authenticated-orcid":false,"given":"Chengyuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7512-3705","authenticated-orcid":false,"given":"Pei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6961-1115","authenticated-orcid":false,"given":"Haotian","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2749-6435","authenticated-orcid":false,"given":"Jiaxi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3286-341X","authenticated-orcid":false,"given":"Hang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keke","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bofeng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5263-8458","authenticated-orcid":false,"given":"Todd","family":"Szymkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2026.108607_b1","article-title":"A Bayesian generalised extreme value model to estimate real-time pedestrian crash risks at signalised intersections using artificial intelligence-based video analytics","volume":"38","author":"Ali","year":"2023","journal-title":"Anal. Methods Accid. Res."},{"key":"10.1016\/j.aap.2026.108607_b2","series-title":"The Global Automotive OEM Telematics Market, 10th Edition","author":"Cederqvist","year":"2025"},{"issue":"1","key":"10.1016\/j.aap.2026.108607_b3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.26599\/JICV.2023.9210004","article-title":"Human-like lane-changing trajectory planning algorithm for human-machine conflict mitigation","volume":"6","author":"Dai","year":"2023","journal-title":"J. Intell. Connect. Veh."},{"issue":"22","key":"10.1016\/j.aap.2026.108607_b4","doi-asserted-by":"crossref","first-page":"14859","DOI":"10.3390\/su142214859","article-title":"A systematic review of traffic incident detection algorithms","volume":"14","author":"ElSahly","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.aap.2026.108607_b5","series-title":"Traffic Incident Management (TIM) Performance Measurement Outreach Fact Sheet","author":"Federal Highway Administration","year":"2010"},{"key":"10.1016\/j.aap.2026.108607_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2025.105283","article-title":"Goal-based neural physics vehicle trajectory prediction model","volume":"179","author":"Gan","year":"2025","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.aap.2026.108607_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122848","article-title":"A lane-level localization method via the lateral displacement estimation model on expressway","volume":"243","author":"Guo","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aap.2026.108607_b8","first-page":"20","volume":"vol. 3","author":"Hancock","year":"2013"},{"issue":"3","key":"10.1016\/j.aap.2026.108607_b9","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIV.2020.3035329","article-title":"Lane-level map matching based on HMM","volume":"6","author":"Hansson","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"1","key":"10.1016\/j.aap.2026.108607_b10","doi-asserted-by":"crossref","first-page":"3985","DOI":"10.1038\/s41467-023-39698-6","article-title":"A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap","volume":"14","author":"Herfort","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.aap.2026.108607_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2019.105392","article-title":"Highway crash detection and risk estimation using deep learning","volume":"135","author":"Huang","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108607_b12","article-title":"Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics","volume":"4","author":"Hussain","year":"2024","journal-title":"Commun. Transp. Res."},{"key":"10.1016\/j.aap.2026.108607_b13","article-title":"Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: A two-stage deep learning modeling framework","volume":"40","author":"Jin","year":"2023","journal-title":"Anal. Methods Accid. Res."},{"key":"10.1016\/j.aap.2026.108607_b14","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.compenvurbsys.2018.11.007","article-title":"Traffic congestion analysis at the turn level using taxis\u2019 GPS trajectory data","volume":"74","author":"Kan","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"issue":"3","key":"10.1016\/j.aap.2026.108607_b15","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/03611981241281740","article-title":"Real-time freeway crash detection framework using connected vehicle waypoint data","volume":"2679","author":"Kandiboina","year":"2025","journal-title":"Transp. Res. Rec."},{"issue":"8","key":"10.1016\/j.aap.2026.108607_b16","doi-asserted-by":"crossref","first-page":"12480","DOI":"10.1109\/TITS.2021.3114816","article-title":"Deep crash detection from vehicular sensor data with multimodal self-supervision","volume":"23","author":"Kubin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.aap.2026.108607_b17","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1177\/03611981241297662","article-title":"Physics-informed neural network model for predictive risk assessment and safety analysis","volume":"2679","author":"Lee","year":"2025","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108607_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106504","article-title":"A hybrid machine learning model for predicting real-time secondary crash likelihood","volume":"165","author":"Li","year":"2022","journal-title":"Accid. Anal. Prev."},{"issue":"7","key":"10.1016\/j.aap.2026.108607_b19","article-title":"Real-time crash likelihood prediction using temporal attention\u2013based deep learning and trajectory fusion","volume":"148","author":"Li","year":"2022","journal-title":"J. Transp. Eng. Part A: Syst."},{"key":"10.1016\/j.aap.2026.108607_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105658","article-title":"The application of novel connected vehicles emulated data on real-time crash potential prediction for arterials","volume":"144","author":"Li","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108607_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2019.105371","article-title":"Real-time crash risk prediction on arterials based on LSTM-CNN","volume":"135","author":"Li","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108607_b22","article-title":"Cost of travel delays caused by traffic crashes","volume":"4","author":"Lian","year":"2024","journal-title":"Commun. Transp. Res."},{"key":"10.1016\/j.aap.2026.108607_b23","series-title":"Theory foundation of physics-enhanced residual learning","author":"Liang","year":"2025"},{"key":"10.1016\/j.aap.2026.108607_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122813","article-title":"Spatiotemporal features of traffic help reduce automatic accident detection time","volume":"244","author":"Moriano","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aap.2026.108607_b25","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.datak.2013.05.002","article-title":"On detection of emerging anomalous traffic patterns using GPS data","volume":"87","author":"Pang","year":"2013","journal-title":"Data Knowl. Eng."},{"key":"10.1016\/j.aap.2026.108607_b26","series-title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation","author":"Powers","year":"2020"},{"issue":"12","key":"10.1016\/j.aap.2026.108607_b27","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.1049\/itr2.12498","article-title":"Towards efficient traffic crash detection based on macro and micro data fusion on expressways: A digital twin framework","volume":"18","author":"Qu","year":"2024","journal-title":"IET Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.aap.2026.108607_b28","article-title":"Expressway traffic incident detection using a deep learning approach based on spatiotemporal features with multilevel fusion","volume":"150","author":"Qu","year":"2024","journal-title":"J. Transp. Eng. Part A: Syst."},{"key":"10.1016\/j.aap.2026.108607_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.107956","article-title":"Crash localization and traffic impact assessment via spatio-temporal analysis of connected vehicle data","volume":"214","author":"Querfurth","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108607_b30","series-title":"2016 IEEE 19th International Conference on Intelligent Transportation Systems","first-page":"1155","article-title":"Lane-level map-matching based on optimization","author":"Rabe","year":"2016"},{"issue":"8","key":"10.1016\/j.aap.2026.108607_b31","doi-asserted-by":"crossref","first-page":"8356","DOI":"10.1109\/TITS.2024.3354852","article-title":"Review of accident detection methods using dashcam videos for autonomous driving vehicles","volume":"25","author":"Rocky","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108607_b32","series-title":"Advance real-time detection of traffic incidents in highways using vehicle trajectory data","author":"Roy","year":"2024"},{"issue":"1","key":"10.1016\/j.aap.2026.108607_b33","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s42421-026-00148-6","article-title":"Advance real-time detection of traffic incidents in highways using vehicle trajectory data","volume":"8","author":"Roy","year":"2026","journal-title":"Data Sci. Transp."},{"key":"10.1016\/j.aap.2026.108607_b34","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jsr.2021.12.007","article-title":"A literature review of machine learning algorithms for crash injury severity prediction","volume":"80","author":"Santos","year":"2022","journal-title":"J. Saf. Res."},{"key":"10.1016\/j.aap.2026.108607_b35","series-title":"2016 International Conference on Connected Vehicles and Expo","first-page":"36","article-title":"S-CarCrash: Real-time crash detection analysis and emergency alert using smartphone","author":"Sharma","year":"2016"},{"key":"10.1016\/j.aap.2026.108607_b36","doi-asserted-by":"crossref","DOI":"10.1177\/03611981251346764","article-title":"Leveraging high-resolution connected vehicle data for incident detection, management, and analysis: An exploratory study","author":"Shi","year":"2025","journal-title":"Transp. Res. Rec."},{"issue":"3","key":"10.1016\/j.aap.2026.108607_b37","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/13658816.2017.1402913","article-title":"Automatic change detection in lane-level road networks using GPS trajectories","volume":"32","author":"Yang","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.aap.2026.108607_b38","series-title":"Real-time crash risk analysis of urban arterials incorporating bluetooth, weather, and adaptive signal control data","author":"Yuan","year":"2018"},{"key":"10.1016\/j.aap.2026.108607_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15472450.2025.2581648","article-title":"Toward explainable automatic incident detection: a spatiotemporal self-supervised framework with multi-probing classifiers","author":"Zhai","year":"2025","journal-title":"J. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108607_b40","article-title":"Real-time crash potential prediction on freeways using connected vehicle data","volume":"36","author":"Zhang","year":"2022","journal-title":"Anal. Methods Accid. Res."},{"issue":"1","key":"10.1016\/j.aap.2026.108607_b41","article-title":"Accident detection and flow prediction for connected and automated transport systems","volume":"2023","author":"Zhang","year":"2023","journal-title":"J. Adv. Transp."},{"key":"10.1016\/j.aap.2026.108607_b42","first-page":"1","article-title":"Incident detection for freeways based on a dual-state traffic factor state network","author":"Zhang","year":"2025","journal-title":"Transp. A: Transp. Sci."},{"issue":"2","key":"10.1016\/j.aap.2026.108607_b43","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1108\/JICV-03-2021-0004","article-title":"Dynamic prediction of traffic incident duration on urban expressways: A deep learning approach based on LSTM and MLP","volume":"4","author":"Zhu","year":"2021","journal-title":"J. Intell. Connect. Veh."}],"container-title":["Accident Analysis & Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526002162?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526002162?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T08:14:54Z","timestamp":1782461694000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457526002162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":43,"alternative-id":["S0001457526002162"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2026.108607","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Real-time lane-level abnormal traffic detection on freeways using sparse telematics data","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2026.108607","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108607"}}