Hybrid modeling of unobserved heterogeneity in Ohio freeway crash severity using RWIS data: A Random Forest-Correlated Mixed Logit approach

Esther Bukuru; Philip F. Balyagati; Thobias Sando; Emmanuel Kidando; Josiah Owusu-Danquah

doi:10.1080/15389588.2026.2663379

Hybrid modeling of unobserved heterogeneity in Ohio freeway crash severity using RWIS data: A Random Forest-Correlated Mixed Logit approach

Esther Bukuru
, Philip F. Balyagati
, Thobias Sando
, Emmanuel Kidando
, Josiah Owusu-Danquah

School of Engineering

Producción científica: Article › revisión exhaustiva

Idioma original	English
Publicación	Traffic Injury Prevention
DOI	https://doi.org/10.1080/15389588.2026.2663379
Estado	Accepted/In press - 2026

ASJC Scopus Subject Areas

Safety Research
Public Health, Environmental and Occupational Health

Acceder al documento

10.1080/15389588.2026.2663379

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title = "Hybrid modeling of unobserved heterogeneity in Ohio freeway crash severity using RWIS data: A Random Forest-Correlated Mixed Logit approach",

keywords = "Correlated Mixed Logit, Random Forest, Road weather Information system (RWIS), crash severity, severity Index, weather-responsive ITS",

author = "Esther Bukuru and Balyagati, \{Philip F.\} and Thobias Sando and Emmanuel Kidando and Josiah Owusu-Danquah",

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year = "2026",

doi = "10.1080/15389588.2026.2663379",

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T2 - A Random Forest-Correlated Mixed Logit approach

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AU - Balyagati, Philip F.

AU - Sando, Thobias

AU - Kidando, Emmanuel

AU - Owusu-Danquah, Josiah

PY - 2026

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KW - Random Forest

KW - Road weather Information system (RWIS)

KW - crash severity

KW - severity Index

KW - weather-responsive ITS

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