{"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":1793467856121,"version":"4.5.0"},"reference-count":38,"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":[[2026,10,30]],"date-time":"2026-10-30T00:00:00Z","timestamp":1793318400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis & Prevention"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.aap.2026.108788","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:18:05Z","timestamp":1790385485000},"page":"108788","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Revealing the behavioral architecture of autonomous vehicle crashes: an interaction-based typology using real-world reports"],"prefix":"10.1016","volume":"238","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3073-3272","authenticated-orcid":false,"given":"Hazem","family":"Al-Mahamid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6727-1540","authenticated-orcid":false,"given":"Adam","family":"Torok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2026.108788_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.geits.2024.100166","article-title":"Towards vehicle electrification: a mathematical prediction of battery electric vehicle ownership growth, the case of Turkey","volume":"3","author":"Alatawneh","year":"2024","journal-title":"Green Energy Intell Transp."},{"key":"10.1016\/j.aap.2026.108788_b0010","doi-asserted-by":"crossref","first-page":"126","DOI":"10.3311\/PPtr.20655","article-title":"Analysis of the emergence of autonomous vehicles using simulation-based dynamic traffic assignment \u2013 the case of Budapest","volume":"51","author":"Alatawneh","year":"2023","journal-title":"Period. Polytech. Transp. Eng."},{"key":"10.1016\/j.aap.2026.108788_b0015","article-title":"Assessing sustainability indicators of public transportation using PAHP","volume":"9","author":"Al-lami","year":"2025","journal-title":"Sustain. Futur."},{"key":"10.1016\/j.aap.2026.108788_b0020","doi-asserted-by":"crossref","first-page":"15289","DOI":"10.3390\/su152115289","article-title":"Sustainability Indicators of Surface Public Transportation","volume":"15","author":"Al-lami","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.aap.2026.108788_b0025","article-title":"A comparative analysis of the human development index and road safety outcomes in the European Union","volume":"2","author":"Al-Mahamid","year":"2025","journal-title":"Eur. Transp. Stud."},{"key":"10.1016\/j.aap.2026.108788_b0030","article-title":"Developing safety performance functions incorporating pavement roughness using Poisson regression and machine learning models on Jordan\u2019s Desert Highway","volume":"34","author":"Al-Mahamid","year":"2025","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"10.1016\/j.aap.2026.108788_b0035","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1177\/03611981211018461","article-title":"Extracting rules from autonomous-vehicle-involved crashes by applying decision tree and association rule methods","volume":"2675","author":"Ashraf","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108788_b0040","doi-asserted-by":"crossref","first-page":"12021","DOI":"10.1038\/s41598-025-91361-w","article-title":"A study on the correlation between MBTI dimensions and driving behavior characteristics","volume":"15","author":"Bai","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aap.2026.108788_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2019.105354","article-title":"Exploratory analysis of automated vehicle crashes in California: a text analytics & hierarchical Bayesian heterogeneity-based approach","volume":"135","author":"Boggs","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0050","unstructured":"California DMV, 2025. Autonomous Vehicle Collision Reports. Calif. DMV. URL https:\/\/www.dmv.ca.gov\/portal\/customer-service\/request-vehicle-or-driver-records\/online-vehicle-record-request\/\/ (accessed 21.05.26)."},{"key":"10.1016\/j.aap.2026.108788_b0055","article-title":"Exploring the mechanism of crashes with autonomous vehicles using machine learning","volume":"2021","author":"Chen","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"10.1016\/j.aap.2026.108788_b0060","unstructured":"DeSeranno, T., 2022. Statistically Significant: The History and Criticism of NHTSA\u2019s \u201c94% Driver Error\u201d Figure | Nelson Law, LLC. URL https:\/\/nelson.legal\/nhtsa-94-percent-driver-error-figure\/ (accessed 11.1.25)."},{"key":"10.1016\/j.aap.2026.108788_b0065","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.2026.108788_b0070","unstructured":"Glover, M., Service, M.N., 2015. California DMV Adopts Autonomous Vehicle Testing Rules [WWW Document]. GovTech. URL https:\/\/www.govtech.com\/transportation\/California-DMV-Adopts-Autonomous-Vehicle-Testing-Rules.html (accessed 11.1.25)."},{"key":"10.1016\/j.aap.2026.108788_b0075","first-page":"493","article-title":"Causation mechanisms in car-to-vulnerable road user crashes: Implications for active safety systems","volume":"49","author":"Habibovic","year":"2012","journal-title":"Accid. Anal. Prev., PTW + Cognitive Impair. Driv. Safety"},{"key":"10.1016\/j.aap.2026.108788_b0080","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1177\/03611981231209319","article-title":"Characteristics of rear-end collisions: a comparison between automated driving system-involved crashes and advanced driving assistance system-involved crashes","volume":"2678","author":"Huang","year":"2024","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108788_b0085","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1177\/03611981241274152","article-title":"Investigating the interrelationships among factors associated with automated vehicle crashes using additive Bayesian network","volume":"2679","author":"Huang","year":"2025","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108788_b0090","article-title":"Modeling automated vehicle crashes with a focus on vehicle at-fault, collision type, and injury outcome","volume":"148","author":"Kutela","year":"2022","journal-title":"J. Transp. Eng. Part Syst."},{"key":"10.1016\/j.aap.2026.108788_b0095","article-title":"The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports","volume":"10","author":"Lee","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.aap.2026.108788_b0100","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, X., Wang, T., Yue, L., Liu, Q., 2025. Characteristics Analysis of Autonomous Vehicle Pre-Crash Scenarios. https:\/\/doi.org\/10.2139\/ssrn.5314785.","DOI":"10.2139\/ssrn.5314785"},{"key":"10.1016\/j.aap.2026.108788_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107383","article-title":"Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections","volume":"195","author":"Liu","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_bib186","doi-asserted-by":"crossref","first-page":"106281","DOI":"10.1016\/j.aap.2021.106281","article-title":"Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology","volume":"159","author":"Liu","year":"2021","journal-title":"Accident Anal. Prevent."},{"key":"10.1016\/j.aap.2026.108788_b0110","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1177\/03611981221095526","article-title":"Impact of different penetration rates of shared autonomous vehicles on traffic: case study of Budapest","volume":"2676","author":"Matalqah","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108788_b0115","unstructured":"Najm, W.G., Smith, J.D., Yanagisawa, M., 2007. Pre-crash scenario typology for crash avoidance research [WWW Document]. URL https:\/\/rosap.ntl.bts.gov (accessed 11.2.25)."},{"key":"10.1016\/j.aap.2026.108788_b0120","unstructured":"Najm, W.G., Toma, S., Brewer, J., 2013. Depiction of priority light-vehicle pre-crash scenarios for safety applications based on vehicle-to-vehicle communications [WWW Document]. URL https:\/\/rosap.ntl.bts.gov (accessed 11.4.25)."},{"key":"10.1016\/j.aap.2026.108788_b0125","unstructured":"NHTSA, 2015. Crash Stats: Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey."},{"key":"10.1016\/j.aap.2026.108788_b0130","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.aap.2017.07.011","article-title":"Pre-crash scenarios at road junctions: a clustering method for car crash data","volume":"107","author":"Nitsche","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107021","article-title":"Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways","volume":"186","author":"Ren","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0140","doi-asserted-by":"crossref","first-page":"11358","DOI":"10.3390\/ijerph191811358","article-title":"Divergent effects of factors on crash severity under autonomous and conventional driving modes using a hierarchical Bayesian approach","volume":"19","author":"Ren","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.aap.2026.108788_b0145","series-title":"2015 IEEE 18th International Conference on Intelligent Transportation Systems. Presented at the 2015 IEEE 18th International Conference on Intelligent Transportation Systems","first-page":"1533","article-title":"Pedestrian and bicyclist crash scenarios in the U.S","author":"Sherony","year":"2015"},{"key":"10.1016\/j.aap.2026.108788_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106017","article-title":"Automated vehicle crash sequences: patterns and potential uses in safety testing","volume":"153","author":"Song","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0155","unstructured":"Swanson, E.D., Foderaro, F., Yanagisawa, M., Najm, W.G., Azeredo, P., 2019. Statistics of Light-Vehicle Pre-Crash Scenarios Based on 2011\u20132015 National Crash Data [WWW Document]. https:\/\/doi.org\/10.21949\/1530164."},{"key":"10.1016\/j.aap.2026.108788_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106821","article-title":"Causation analysis of crashes and near crashes using naturalistic driving data","volume":"177","author":"Wang","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0165","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jsr.2019.09.001","article-title":"Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes","volume":"71","author":"Xu","year":"2019","journal-title":"J. Safety Res."},{"key":"10.1016\/j.aap.2026.108788_b0170","first-page":"1061","article-title":"A comparison of patterns and contributing factors of ADAS and ADS involved crashes","volume":"16","author":"Yan","year":"2024","journal-title":"J. Transp. Saf. Secur."},{"key":"10.1016\/j.aap.2026.108788_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107404","article-title":"Pedestrian crash causation analysis and active safety system calibration","volume":"195","author":"Ye","year":"2024","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2026.108788_b0180","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1177\/0361198120925472","article-title":"Identifying pedestrian crash contributing factors using association analysis and their implications for development of active pedestrian safety system","volume":"2674","author":"Yue","year":"2020","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2026.108788_b0185","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jsr.2020.02.020","article-title":"In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention","volume":"73","author":"Yue","year":"2020","journal-title":"J. Safety Res."}],"container-title":["Accident Analysis & Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003970?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457526003970?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:01:43Z","timestamp":1793466103000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457526003970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":38,"alternative-id":["S0001457526003970"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2026.108788","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":"Revealing the behavioral architecture of autonomous vehicle crashes: an interaction-based typology using real-world reports","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2026.108788","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108788"}}