{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:30:56Z","timestamp":1793467856690,"version":"4.5.0"},"reference-count":69,"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\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2025YJS061"],"award-info":[{"award-number":["2025YJS061"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62333016"],"award-info":[{"award-number":["62333016"]}],"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":["72288101"],"award-info":[{"award-number":["72288101"]}],"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.2026.108750","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T07:35:43Z","timestamp":1788766543000},"page":"108750","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A two-stage driving-style-stratified framework for analyzing and predicting car-following conflict risk in mixed traffic"],"prefix":"10.1016","volume":"238","author":[{"given":"Manli","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5545-4128","authenticated-orcid":false,"given":"Xiaobao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiteng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqi","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2026.108750_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107088","article-title":"Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections","volume":"187","author":"Adavikottu","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0010","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.trf.2022.11.006","article-title":"Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following","volume":"92","author":"Adavikottu","year":"2023","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.trd.2023.103964","article-title":"Impact of vehicle arrangement in mixed autonomy traffic on emissions","volume":"125","author":"Alhariqi","year":"2023","journal-title":"Transp. Res. Part D: Transp. Environ."},{"key":"10.1016\/j.aap.2026.108750_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2024.115391","article-title":"Real-time rear-end conflict prediction on congested highways sections using trajectory data","volume":"187","author":"An","year":"2024","journal-title":"Chaos, Solitons Fractals"},{"key":"10.1016\/j.aap.2026.108750_b0025","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.aap.2019.01.014","article-title":"How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data","volume":"127","author":"Arvin","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107897","article-title":"Conflict resolution behavior of autonomous vehicles at intersections under mixed traffic environment","volume":"211","author":"Ashraf","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105762","article-title":"An examination of teen drivers\u2019 car-following behavior under naturalistic driving conditions: With and without an advanced driving assistance system","volume":"147","author":"Bao","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2026.105637","article-title":"A physics-informed uncertainty quantification framework for deep learning-based real-time lane-change intention prediction","volume":"187","author":"Cao","year":"2026","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108750_b0045","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.trf.2025.03.023","article-title":"Driving among autonomous vehicles: The effect of initial trust and driving style on driving behaviors","volume":"112","author":"Cegarra","year":"2025","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107530","article-title":"Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform","volume":"199","author":"Chen","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105871","article-title":"Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing","volume":"151","author":"Chen","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0060","article-title":"Modeling accident risks in different lane-changing behavioral patterns","volume":"30","author":"Chen","year":"2021","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108750_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2026.132182","article-title":"An interpretable end-to-end hybrid deep learning car-following model: Impact analysis of driving styles and heterogeneous vehicle interactions","volume":"319","author":"Chu","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aap.2026.108750_b0070","article-title":"Fuzzy inference system design for promoting an eco-friendly driving style in IoV domain","volume":"34","author":"De Rango","year":"2022","journal-title":"Veh. Commun."},{"key":"10.1016\/j.aap.2026.108750_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.104096","article-title":"Dynamic clustering analysis for driving styles identification","volume":"97","author":"De Zepeda","year":"2021","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aap.2026.108750_b0080","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.trf.2017.06.004","article-title":"How to define the accident risk level of car drivers by combining objective and subjective measures of driving style","volume":"49","author":"Eboli","year":"2017","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0085","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.trf.2020.07.010","article-title":"Cross-cultural perspective of driving style in young adults: Psychometric evaluation through the analysis of the Multidimensional Driving Style Inventory","volume":"73","author":"Freuli","year":"2020","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0090","article-title":"New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records","volume":"38","author":"Fu","year":"2025","journal-title":"Travel Behav. Soc."},{"key":"10.1016\/j.aap.2026.108750_b0095","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.trf.2022.01.011","article-title":"Psychometric properties of driver self-image inventory for Chinese drivers and its associations with personality and driving style","volume":"85","author":"Guo","year":"2022","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2025.110489","article-title":"Styles energy consumption analysis of lane-changing maneuvers in autonomous vehicles: The role of driving styles","volume":"165","author":"Guo","year":"2025","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.aap.2026.108750_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.107963","article-title":"Multi-scenario driving style research based on driving behavior pattern extraction","volume":"214","author":"He","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2025.130519","article-title":"Car following dynamics in mixed traffic flow of autonomous and human-driven vehicles: Complex networks approach","volume":"665","author":"Hu","year":"2025","journal-title":"Physica A"},{"issue":"6","key":"10.1016\/j.aap.2026.108750_b0115","doi-asserted-by":"crossref","first-page":"6711","DOI":"10.1109\/TITS.2023.3258145","article-title":"Autonomous Vehicle\u2019s Impact on Traffic: Empirical Evidence From Waymo Open Dataset and Implications From Modelling","volume":"24","author":"Hu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108750_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103490","article-title":"Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research","volume":"134","author":"Hu","year":"2022","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108750_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.tra.2025.104520","article-title":"Examining the temporary use behavior of autonomous vehicles under uncertainty: A stated preference analysis","volume":"198","author":"Hu","year":"2025","journal-title":"Transp. Res. A Policy Pract."},{"key":"10.1016\/j.aap.2026.108750_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2025.106385","article-title":"Combining sequential Gaussian co-simulation and Monte Carlo dropout-based deep learning models for geochemical anomaly detection and uncertainty assessment","volume":"184","author":"Huang","year":"2025","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.aap.2026.108750_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107616","article-title":"Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data","volume":"203","author":"Huang","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0140","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.trf.2025.06.004","article-title":"Unraveling the impact of autonomous driving threat perception on risky driving","volume":"114","author":"Huang","year":"2025","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104673","article-title":"Beyond behavioural change: Investigating alternative explanations for shorter time headways when human drivers follow automated vehicles","volume":"164","author":"Jiao","year":"2024","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108750_b0150","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"},{"issue":"10","key":"10.1016\/j.aap.2026.108750_b0155","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TITS.2017.2769158","article-title":"A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions","volume":"19","author":"Katrakazas","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aap.2026.108750_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106086","article-title":"Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment","volume":"156","author":"Khattak","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0165","series-title":"Large Car-Following Data Based on Lyft Level-5 Open Dataset: Following Autonomous Vehicles vs. Human-Driven Vehicles","first-page":"5818","author":"Li","year":"2023"},{"key":"10.1016\/j.aap.2026.108750_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.107982","article-title":"Car-following safety modeling and risk assessment of autonomous vehicle in icy and snowy weather","volume":"214","author":"Li","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0175","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.2026.108750_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2023.129360","article-title":"Effect of autonomous vehicles on car-following behavior of human drivers: Analysis based on structural equation models","volume":"633","author":"Li","year":"2024","journal-title":"Physica A"},{"key":"10.1016\/j.aap.2026.108750_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112165","article-title":"Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes","volume":"166","author":"Liu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.aap.2026.108750_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107904","article-title":"Assessing the impact of car-following driving style on traffic conflict risk using asymmetric behavior model and explainable machine learning","volume":"211","author":"Ma","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106096","article-title":"Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry","volume":"154","author":"Ma","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106006","article-title":"Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles","volume":"152","author":"Mahdinia","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0205","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ssci.2019.01.025","article-title":"Identifying driving safety profiles from smartphone data using unsupervised learning","volume":"119","author":"Mantouka","year":"2019","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aap.2026.108750_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102917","article-title":"Classifying travelers\u2019 driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning","volume":"122","author":"Mohammadnazar","year":"2021","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aap.2026.108750_b0215","unstructured":"NHTSA, 2025. National Highway Traffic Safety Administration (2025). Standing General Order 2021-01 Incident Report.For incidents involving ADS and Level 2 ADAS. U.S. Department of Transportation."},{"key":"10.1016\/j.aap.2026.108750_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2024.106516","article-title":"The prevention of road accidents in non-expert drivers: Exploring the influence of Theory of Mind and driving style","volume":"175","author":"Nori","year":"2024","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aap.2026.108750_b0225","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.aap.2018.12.019","article-title":"Evaluating the safety impact of connected and autonomous vehicles on motorways","volume":"124","author":"Papadoulis","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107827","article-title":"Heterogeneity in crash patterns of autonomous vehicles: The latent class analysis coupled with multinomial logit model","volume":"209","author":"Ren","year":"2025","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105499","article-title":"Investigating rear-end collision avoidance behavior under varied foggy weather conditions: A study using advanced driving simulator and survival analysis","volume":"139","author":"Shangguan","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0240","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/5551273","article-title":"Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure","volume":"2021","author":"Shangguan","year":"2021","journal-title":"J. Adv. Transp."},{"key":"10.1016\/j.aap.2026.108750_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106122","article-title":"An integrated methodology for real-time driving risk status prediction using naturalistic driving data","volume":"156","author":"Shangguan","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0250","article-title":"An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances","volume":"38","author":"Shangguan","year":"2023","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108750_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105567","article-title":"Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles","volume":"142","author":"Sinha","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0260","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.trf.2022.02.002","article-title":"Behavioral adaptations of human drivers interacting with automated vehicles","volume":"86","author":"Soni","year":"2022","journal-title":"Transport. Res. F: Traffic Psychol. Behav."},{"key":"10.1016\/j.aap.2026.108750_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2024.107623","article-title":"Greater prosociality toward other human drivers than autonomous vehicles: Human drivers\u2019 discriminatory behavior in mixed traffic","volume":"203","author":"Sun","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0270","author":"Wang","year":"2023","journal-title":"Calibration in Deep Learning: A Survey of the State-of-the-Art."},{"key":"10.1016\/j.aap.2026.108750_b0275","author":"Wang","year":"2017","journal-title":"Deep & Cross Network for Ad Click Predictions."},{"key":"10.1016\/j.aap.2026.108750_b0280","doi-asserted-by":"crossref","unstructured":"Wang, R., Shivanna, R., Cheng, D.Z., Jain, S., Lin, D., Hong, L., Chi, E.H., 2020. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. doi:10.48550\/ARXIV.2008.13535.","DOI":"10.1145\/3442381.3450078"},{"key":"10.1016\/j.aap.2026.108750_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.130985","article-title":"Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style","volume":"343","author":"Wang","year":"2022","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.aap.2026.108750_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106502","article-title":"Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study","volume":"164","author":"Wang","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106689","article-title":"Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset","volume":"172","author":"Wen","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2023.102453","article-title":"HUDNet: A dynamic calibration method for automotive augmented reality head-up-displays","volume":"78","author":"Xu","year":"2023","journal-title":"Displays"},{"key":"10.1016\/j.aap.2026.108750_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2025.108286","article-title":"Influence factors and model selection for conflict risk in different car-following behaviors: Insights from automated and human-driven vehicles","volume":"224","author":"Yang","year":"2026","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0310","article-title":"Investigation on car-following heterogeneity and its impacts on traffic safety and sustainability","volume":"1\u201325","author":"Yao","year":"2024","journal-title":"Transportmetrica A: Transport Science"},{"key":"10.1016\/j.aap.2026.108750_b0315","article-title":"Fusion convolutional neural network-based interpretation of unobserved heterogeneous factors in driver injury severity outcomes in single-vehicle crashes","volume":"30","author":"Yu","year":"2021","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108750_b0320","article-title":"Analysis of injury severity of rear-end crashes in work zones: A random parameters approach with heterogeneity in means and variances","volume":"27","author":"Yu","year":"2020","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108750_b0325","article-title":"Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis","volume":"35","author":"Yuan","year":"2022","journal-title":"Anal. Methods Accid. Res"},{"key":"10.1016\/j.aap.2026.108750_b0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107445","article-title":"How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset","volume":"196","author":"Zang","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108750_b0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.tra.2023.103887","article-title":"Public perception of autonomous vehicle capability determines judgment of blame and trust in road traffic accidents","volume":"179","author":"Zhang","year":"2024","journal-title":"Transp. Res. A Policy Pract."},{"issue":"11","key":"10.1016\/j.aap.2026.108750_b0340","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1177\/03611981221092402","article-title":"Design, Implementation, and Evaluation of a Roadside Cooperative Perception System","volume":"2676","author":"Zhang","year":"2022","journal-title":"Transportation Research Record: Journal of the Transportation Research Board"},{"key":"10.1016\/j.aap.2026.108750_b0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107072","article-title":"A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles","volume":"188","author":"Zhang","year":"2023","journal-title":"Accid. Anal. Prev."}],"container-title":["Accident Analysis &amp; Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003593?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003593?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:01:56Z","timestamp":1793466116000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457526003593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":69,"alternative-id":["S0001457526003593"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2026.108750","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":"A two-stage driving-style-stratified framework for analyzing and predicting car-following conflict risk in mixed traffic","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2026.108750","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":"108750"}}