A Novel Cluster-Based Multinomial Logit Modeling for Crash Severity Prediction in Collisions Involving Automated EV-Only Manufacturer (AEVOM) Vehicles

A Novel Cluster-Based Multinomial Logit Modeling for Crash Severity Prediction in Collisions Involving Automated EV-Only Manufacturer (AEVOM) Vehicles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Cluster-Based Multinomial Logit Modeling for Crash Severity Prediction in Collisions Involving Automated EV-Only Manufacturer (AEVOM) Vehicles Tausif Islam Chowdhury, Swastika Barua, Sharif Ahmed Rafat, Shriyank Somvanshi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9145643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The increasing prevalence of Automated Electric Vehicles-Only Manufacturer (AEVOM) vehicles underscores the need for better understanding of crash severity under partial automation. Utilizing police-reported crash data from Texas between 2017 and 2024, this study applies a two-stage analytical framework to capture heterogeneity in crash outcomes. Cluster Correspondence Analysis (CCA) is first used to classify crashes into four distinct typologies: high-speed highway collisions, intersection-related events, low-speed impacts with fixed objects, and parked-vehicle crashes in non-trafficway environments. Variable selection and clustering validity are supported by XGBoost (Extreme Gradient Boosting) feature importance metrics and the Creemer’s V statistic. Within each cluster, Random Parameter Logit (RPL) and RPL with Heterogeneity in Means (RPLHM) models are estimated to account for unobserved heterogeneity in crash severity determinants. The analysis reveals that key variables such as lighting conditions, road classification, driver age, seatbelt use, and vehicle type influence severity outcomes differently across clusters. Notably, severe injuries are observed even in low-speed or seemingly controlled environments, highlighting functional limitations in current AEVOM vehicles automation systems. This framework improves model fit and interpretability relative to aggregate models and provides actionable insights for the advancement of advanced driver-assistance systems, infrastructure design, and policy strategies aimed at enhancing the safety of semi-AEVs. Physical sciences/Engineering Physical sciences/Mathematics and computing Automated Electric Vehicle-Only Manufacturer (AEVOM) Crash Severity Analysis Cluster Correspondence Analysis (CCA) Random Parameter Multinomial Logit (RPL) Model Full Text Additional Declarations No competing interests reported. Supplementary Files AuthorStatement.docx DoI.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 17 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9145643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614040453,"identity":"328ae16a-22bd-4de6-92ef-1d9539f3f8ad","order_by":0,"name":"Tausif Islam 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