Decoding Pedestrian Severity at Crosswalks using Hybrid Clustering and Random Parameter Models

Decoding Pedestrian Severity at Crosswalks using Hybrid Clustering and Random Parameter Models | 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 Decoding Pedestrian Severity at Crosswalks using Hybrid Clustering and Random Parameter Models Swastika Barua, Michael Starewich, Tausif Islam Chowdhury, Amir Rafe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003568/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 Pedestrian crashes at crosswalks represent a critical safety concern due to their disproportionate contribution to severe injuries and fatalities relative to their overall frequency. This study analyzes pedestrian-involved crosswalk crashes reported in the Texas Crash Records Information System (CRIS) from 2017 to 2022 to examine context-specific determinants of injury severity. A two-stage analytical framework was employed. First, Cluster Correspondence Analysis (CCA) identified three distinct crash environments: (1) intersection crashes associated with turn-phase right-of-way violations, (2) low-speed yield-phase property-damage-only collisions at intersections, and (3) distraction-related driveway departure crashes at non-intersections. Each cluster exhibited unique combinations of movement patterns, roadway characteristics, and behavioral attributes. Second, cluster-specific Random Parameter Logit with Heterogeneity in Means (RPLHM) and Multinomial Logit (MNL) models were estimated to evaluate injury severity determinants within each context. Results indicate that weather, lighting conditions, roadway functional type, posted speed limits, and inattentive driving significantly influence severity outcomes, with effects varying across clusters. Several parameters, including daylight conditions and undivided roadway configurations, demonstrated substantial unobserved heterogeneity, suggesting context-dependent risk shifts influenced by roadway environment and demographic factors. These findings highlight the limitations of uniform countermeasures and support cluster-specific, context-sensitive interventions to mitigate pedestrian injury severity at crosswalks. Physical sciences/Engineering Physical sciences/Mathematics and computing Health sciences/Risk factors Cluster Correspondence Analysis Random Parameter Logit Crosswalk Full Text Additional Declarations No competing interests reported. Supplementary Files AuthorStatement.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 24 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-9003568","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624186468,"identity":"5615162d-a013-4335-8254-d33d9b78461e","order_by":0,"name":"Swastika Barua","email":"","orcid":"","institution":"Texas State University","correspondingAuthor":false,"prefix":"","firstName":"Swastika","middleName":"","lastName":"Barua","suffix":""},{"id":624186469,"identity":"fb18dbe6-dc44-4233-a93e-634e743b9d68","order_by":1,"name":"Michael Starewich","email":"","orcid":"","institution":"Texas State 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