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Eng., Part a: Syst."},{"key":"10.1016\/j.aap.2025.108352_b0325","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s40534-017-0129-7","article-title":"Real-time crash prediction on freeways using data mining and emerging techniques","volume":"25","author":"You","year":"2017","journal-title":"J. Modern Transport."},{"key":"10.1016\/j.aap.2025.108352_b0330","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jsr.2013.05.002","article-title":"Investigating the different characteristics of weekday and weekend crashes","volume":"46","author":"Yu","year":"2013","journal-title":"J. Saf. Res."},{"key":"10.1016\/j.aap.2025.108352_b0335","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.aap.2012.11.027","article-title":"Utilizing support vector machine in real-time crash risk evaluation","volume":"51","author":"Yu","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2025.108352_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102740","article-title":"Convolutional neural networks with refined loss functions for the real-time crash risk analysis","volume":"119","author":"Yu","year":"2020","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2025.108352_b0345","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.aap.2018.07.031","article-title":"Approach-level real-time crash risk analysis for signalized intersections","volume":"119","author":"Yuan","year":"2018","journal-title":"Accid. Anal. Prev."},{"issue":"4","key":"10.1016\/j.aap.2025.108352_b0350","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1177\/0361198119840611","article-title":"Real-time crash risk prediction using long short-term memory recurrent neural network","volume":"2673","author":"Yuan","year":"2019","journal-title":"Transp. Res. Rec."},{"issue":"2","key":"10.1016\/j.aap.2025.108352_b0355","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0262941","article-title":"Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents","volume":"17","author":"Zhang","year":"2022","journal-title":"PLoS One"},{"issue":"10","key":"10.1016\/j.aap.2025.108352_b0360","doi-asserted-by":"crossref","first-page":"19187","DOI":"10.1109\/TITS.2022.3170628","article-title":"A xgboost-based lane change prediction on time series data using feature engineering for autopilot vehicles","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. 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