{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:31:03Z","timestamp":1793467863590,"version":"4.5.0"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52525204"],"award-info":[{"award-number":["52525204"]}],"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":["52572354"],"award-info":[{"award-number":["52572354"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,12]]},"DOI":"10.1016\/j.aap.2026.108744","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:00:00Z","timestamp":1788537600000},"page":"108744","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Jointly predicting secondary crash occurrence and spatiotemporal location via generative adversarial networks under data imbalance"],"prefix":"10.1016","volume":"238","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3531-1082","authenticated-orcid":false,"given":"Junlan","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyu","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiucheng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyuan","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2026.108744_b0005","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.2026.108744_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107205","article-title":"Spatial and temporal prediction of secondary crashes combining stacked sparse auto-encoder and long short-term memory","volume":"191","author":"Li","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108387","article-title":"Spatial-temporal gated transformer network for freeway secondary crash prediction considering the impact of class imbalance","volume":"227","author":"Deng","year":"2026","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108338","article-title":"Solution to data imbalance and complex interactions in traffic conflict modeling: a hypergraph and generative AI approach","volume":"226","author":"Guan","year":"2026","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0025","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."},{"key":"10.1016\/j.aap.2026.108744_b0030","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.trc.2016.08.015","article-title":"Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data","volume":"71","author":"Xu","year":"2016","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108744_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104642","article-title":"A novel generative adversarial network for improving crash severity modeling with imbalanced data","volume":"164","author":"Chen","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108744_b0040","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.isprsjprs.2026.03.049","article-title":"Bridging street view coverage disparities through geographic identity preserving generation from satellite view","volume":"236","author":"Li","year":"2026","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.aap.2026.108744_b0045","article-title":"Time-series generative adversarial networks","volume":"32","author":"Yoon","year":"2019","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.aap.2026.108744_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111079","article-title":"Hybrid variational autoencoder for time series forecasting","volume":"281","author":"Cai","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aap.2026.108744_b0055","unstructured":"Desai, A., C. Freeman, Z. Wang, and I. Beaver. TimeVAE: A variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095, 2021."},{"key":"10.1016\/j.aap.2026.108744_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2026.108508","article-title":"Traffic crash data augmentation with multi-type variables using hybrid VAE-Diffusion generative neural networks for enhancing crash frequency modeling","volume":"231","author":"Chen","year":"2026","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0065","doi-asserted-by":"crossref","unstructured":"Lin, Z., A. Jain, C. Wang, G. Fanti, and V. Sekar. Using GANs for sharing networked time series data: Challenges, initial promise, and open questions.In Proceedings of the ACM Internet Measurement Conference, 2020. pp. 464-483.","DOI":"10.1145\/3419394.3423643"},{"issue":"2","key":"10.1016\/j.aap.2026.108744_b0070","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1007\/s10489-023-05250-8","article-title":"RSMformer: an efficient multiscale transformer-based framework for long sequence time-series forecasting","volume":"54","author":"Tong","year":"2024","journal-title":"Appl. Intell."},{"key":"10.1016\/j.aap.2026.108744_b0075","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.aap.2026.108744_b0080","doi-asserted-by":"crossref","first-page":"e523","DOI":"10.7717\/peerj-cs.523","article-title":"A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach","volume":"7","author":"Alhudhaif","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.aap.2026.108744_b0085","article-title":"Data-efficient object detection on construction sites using reweighting mechanism and cross-batch contrastive learning","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"24","key":"10.1016\/j.aap.2026.108744_b0090","doi-asserted-by":"crossref","first-page":"13123","DOI":"10.3390\/app132413123","article-title":"Remaha\u2013CatBoost: Addressing imbalanced data in traffic accident prediction tasks","volume":"13","author":"Li","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aap.2026.108744_b0095","article-title":"Accounting for temporal instability in macro-level crash frequency modeling: A framework integrating high-resolution traffic dynamic patterns and spatiotemporal approaches","author":"Liu","year":"2025","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108744_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107340","article-title":"Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph convolutional networks","volume":"194","author":"Zhao","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107647","article-title":"Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data","volume":"204","author":"Xie","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108176","article-title":"Analysis of secondary risks induced by defensive braking in autonomous vehicles: a study based on stochastic distribution of drivers","volume":"220","author":"Liu","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0115","unstructured":"Xiao, B. The Signature-Wasserstein GAN for Time Series Generation and Beyond.In, No. Ph.D. Thesis, UCL (University College London), 2023."},{"key":"10.1016\/j.aap.2026.108744_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127864","article-title":"TSGAN: A two-stage interpretable learning method for image cartoonization","volume":"596","author":"Dong","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aap.2026.108744_b0125","unstructured":"Li, X., A. Ngu, and V. Metsis. TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation. arXiv preprint arXiv:2206.13676."},{"key":"10.1016\/j.aap.2026.108744_b0130","unstructured":"Lee, D., S. Malacarne, and E. Aune. Vector quantized time series generation with a bidirectional prior model. arXiv preprint arXiv:2303.04743, 2023."},{"key":"10.1016\/j.aap.2026.108744_b0135","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.aap.2019.02.008","article-title":"A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset","volume":"127","author":"Schl\u00f6gl","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0140","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","article-title":"Clustering-based undersampling in class-imbalanced data","volume":"409","author":"Lin","year":"2017","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0145","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40537-023-00857-7","article-title":"Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data","volume":"11","author":"Yang","year":"2024","journal-title":"Journal of Big Data"},{"key":"10.1016\/j.aap.2026.108744_b0150","doi-asserted-by":"crossref","unstructured":"Park, N., M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim. Data synthesis based on generative adversarial networks. arXiv preprint arXiv:1806.03384, 2018.","DOI":"10.14778\/3231751.3231757"},{"issue":"3","key":"10.1016\/j.aap.2026.108744_b0155","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1093\/jamia\/ocy142","article-title":"Synthesizing electronic health records using improved generative adversarial networks","volume":"26","author":"Baowaly","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0160","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3141\/1581-07","article-title":"Occurrence of secondary crashes on urban arterial roadways","volume":"1581","author":"Raub","year":"1997","journal-title":"Transp. Res. Rec."},{"issue":"3","key":"10.1016\/j.aap.2026.108744_b0165","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1061\/(ASCE)0733-947X(2004)130:3(280)","article-title":"Secondary accident rates on Los Angeles freeways","volume":"130","author":"Moore","year":"2004","journal-title":"J. Transp. Eng."},{"key":"10.1016\/j.aap.2026.108744_b0170","unstructured":"Hirunyanitiwattana, W., and S. P. Mattingly. Identifying secondary crash characteristics for California highway system. Presented at Transportation Research Board 85th Annual Meeting, Washington, DC 2006."},{"key":"10.1016\/j.aap.2026.108744_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107573","article-title":"Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes","volume":"201","author":"Wang","year":"2024","journal-title":"Accid. Anal. Prev."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0180","doi-asserted-by":"crossref","first-page":"68","DOI":"10.3141\/2102-09","article-title":"Identifying secondary crashes and their contributing factors","volume":"2102","author":"Zhan","year":"2009","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108744_b0185","doi-asserted-by":"crossref","unstructured":"Zheng, D., M. V. Chitturi, A. R. Bill, and D. Noyce. Secondary crash identification on a large-scale highway system.In Transportation Research Board 93rd Annual Meeting, 2014.","DOI":"10.3141\/2432-10"},{"key":"10.1016\/j.aap.2026.108744_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108248","article-title":"Identifying disorder and abnormal incidents in traffic flow: a thermodynamics approach based on differential velocity entropy","volume":"223","author":"Ye","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2026.108579","article-title":"LLM-enhanced causal graph learning for real-time crash risk prediction","volume":"233","author":"Li","year":"2026","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108744_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108312","article-title":"Collision risk identification and prediction considering heterogeneous braking patterns using large-scale pre-collision trajectories","volume":"225","author":"Ren","year":"2026","journal-title":"Accid. Anal. Prev."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0205","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3141\/2635-02","article-title":"Probability of secondary crash occurrence on freeways with the use of private-sector speed data","volume":"2635","author":"Goodall","year":"2017","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108744_b0210","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.aap.2018.07.003","article-title":"Likelihood estimation of secondary crashes using Bayesian complementary log-log model","volume":"119","author":"Kitali","year":"2018","journal-title":"Accid. Anal. Prev."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0215","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s12544-026-00822-4","article-title":"Rethinking crash risk prediction: classification versus probabilistic perspectives","volume":"18","author":"Zhang","year":"2026","journal-title":"Eur. Transp. Res. Rev."},{"key":"10.1016\/j.aap.2026.108744_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.tranpol.2026.104149","article-title":"Investigating the impact of flexible working arrangements on departure time choice and time-based congestion charging","author":"Zubair","year":"2026","journal-title":"Transp. Policy"},{"key":"10.1016\/j.aap.2026.108744_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.multra.2026.100306","article-title":"An integrated mutual-information clustering and deep neural network framework for forecasting travel behaviour under flexible working arrangements.","author":"Zubair","year":"2026","journal-title":"Multim. Trans."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0230","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/JICV.2024.9210045","article-title":"Long-term trajectory prediction method based on highway vehicle-following behavior patterns","volume":"8","author":"An","year":"2025","journal-title":"J. Intell. Connect. Vehicles"},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0235","doi-asserted-by":"crossref","first-page":"19","DOI":"10.26599\/JICV.2023.9210025","article-title":"Intelligent decision-making method for vehicles in emergency conditions based on artificial potential fields and finite state machines","volume":"7","author":"Zheng","year":"2024","journal-title":"J. Intell. Connect. Vehicles"},{"key":"10.1016\/j.aap.2026.108744_b0240","article-title":"Machine learning-based real-time crash risk forecasting for pedestrians","volume":"5","author":"Hussain","year":"2025","journal-title":"Commun. Transp. Res."},{"issue":"3","key":"10.1016\/j.aap.2026.108744_b0245","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3760525","article-title":"Vision technologies with applications in traffic surveillance systems: a holistic survey","volume":"58","author":"Zhou","year":"2025","journal-title":"ACM Comput. Surv."},{"issue":"9","key":"10.1016\/j.aap.2026.108744_b0250","doi-asserted-by":"crossref","first-page":"2528","DOI":"10.1109\/TITS.2016.2519043","article-title":"A stochastic emergency response location model considering secondary incidents on freeways","volume":"17","author":"Park","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"10.1016\/j.aap.2026.108744_b0255","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/7833555","article-title":"Time and distance gaps of primary\u2010secondary crashes prediction and analysis using random forests and shap model","volume":"2023","author":"Liu","year":"2023","journal-title":"J. Adv. Transp."},{"key":"10.1016\/j.aap.2026.108744_b0260","unstructured":"Esteban, C., S. L. Hyland, and G. R\u00e4tsch. Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633, 2017."},{"key":"10.1016\/j.aap.2026.108744_b0265","unstructured":"Mirza, M., and S. Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014."},{"key":"10.1016\/j.aap.2026.108744_b0270","unstructured":"Arjovsky, M., S. Chintala, and L. Bottou. Wasserstein generative adversarial networks.In International Conference on Machine Learning, PMLR, 2017. pp. 214-223."},{"key":"10.1016\/j.aap.2026.108744_b0275","article-title":"Improved training of Wasserstein GANs","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.aap.2026.108744_b0280","article-title":"The power of two samples in generative adversarial networks","volume":"31","author":"Lin","year":"2018","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.aap.2026.108744_b0285","unstructured":"Ishfaq, H., A. Hoogi, and D. Rubin. TVAE: Deep metric learning approach for variational autoencoder.In Proc. ICLR Workshop. Vancouver, BC, Canada, 2018."}],"container-title":["Accident Analysis &amp; Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003532?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003532?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:01:07Z","timestamp":1793466067000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457526003532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":57,"alternative-id":["S0001457526003532"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2026.108744","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Jointly predicting secondary crash occurrence and spatiotemporal location via generative adversarial networks under data imbalance","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2026.108744","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":"108744"}}