Capturing Road-Level Heterogeneity in Crash Severity on Rural Two-Lane Highways: A Multilevel Statistical and Machine-Learning Analysis

[2508.09941] Capturing Road-Level Heterogeneity in Crash Severity on Rural Two-Lane Highways: A Multilevel Statistical and Machine-Learning Analysis Skip to main content Search Submit Donate Log in Statistics > Applications arXiv:2508.09941 (stat) [Submitted on 13 Aug 2025 ( v1 ), last revised 24 Aug 2026 (this version, v2)] Title: Capturing Road-Level Heterogeneity in Crash Severity on Rural Two-Lane Highways: A Multilevel Statistical and Machine-Learning Analysis Authors: Mahdi Azhdari , Ali Tavakoli Kashani , Saeideh Amirifar , Amirhossein Taheri , Gerd Müller View PDF HTML (experimental) Abstract: Crash severity may vary across roads because crashes on the same road share contextual characteristics not fully represented by observed variables. This study examined road-level heterogeneity using 19,956 police-reported crashes from 100 rural two-lane highways in Iran during 2021-2024. Single-level logistic regression was compared with random-intercept and random-coefficient multilevel logistic models. A regression-based mixed-effects random forest (MERF) was also used for an exploratory assessment of nonlinear predictive relationships and road-level patterns. The null multilevel model produced an intraclass correlation coefficient of 20.3 percent, indicating meaningful latent between-road variation in crash severity. Pavement condition had the largest estimated random-slope variance, followed by lighting condition, driver education, and driver age. Logistic road-level random intercepts ranged from -2.87 to +1.51 on the log-odds scale. In a comparison excluding road-specific information, the ten-variable MERF random-forest component achieved an AUC of 0.701, compared with 0.638 for single-level logistic regression. Retaining MERF road-level corrections for roads represented during training increased AUC to 0.763. MERF road-level corrections were strongly associated with logistic random intercepts (Pearson r = 0.892; Spearman rho = 0.931), indicating similar relative road patterns, although the estimates are not numerically equivalent. The findings support complementary use of multilevel and nonlinear models. Road-level estimates may help prioritize further investigation, but they do not measure crash frequency, total road risk, or causal effects. Comments: 23 pages, 6 figures Subjects: Applications (stat.AP) Cite as: arXiv:2508.09941 [stat.AP] (or arXiv:2508.09941v2 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2508.09941 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mahdi Azhdari [ view email ] [v1] Wed, 13 Aug 2025 16:51:25 UTC (433 KB) [v2] Mon, 24 Aug 2026 22:37:41 UTC (2,310 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: stat.AP < prev | next > new | recent | 2025-08 Change to browse by: stat References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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