Analysis of the Severity of Run-Off-Road Crashes Using a Mixed Logit Model

Document Type : Original Article

Author
Hamed Saify
Assistant Professor, Department of Civil Engineering, Payame Noor University, Tehran, Iran.
10.22034/road.2025.495172.2349
Abstract
Run-off-road (ROR) Crashes are one of the most significant types of road Crashes, yet there has been limited research on this subject. This study aims to analyze the severity of ROR Crashes by examining a dataset of Crashes over a three-year period (2021 to 2023) on rural roads in East Azarbaijan Province using a mixed logit model (ML), also known as a random parameter logit model. The mixed logit model is highly flexible as it can estimate any random effects model. This flexibility allows for heterogeneous effects and correlation in unobserved factors. To estimate the model, the maximum likelihood method was used with 1000 Halton draws, and marginal effects were calculated to determine the impact of the estimated parameters on Crash severity. Additionally, the log-likelihood ratio was employed to test the model's fit. Due to dataset limitations, variables such as winter season, precipitation, roadside obstacles, presence of guardrails, individuals involved in Crashes, rollovers, vehicle type, age, and gender were considered significant. The modeling results indicate that the variables of individuals involved, rollover, extreme ages of passengers (young and elderly), and the winter season have a direct relationship with more severe injuries in ROR Crashes.
Keywords
Crashes
Modeling
Run-Off-Road
Mixed Logit Model
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