Modeling Severities of Gravel Road Crashes Using Random Parameters

Gravel roads crashes are a roadway safety hazard and such crashes have higher probability of fatalities and serious injuries when compared to paved roads crashes. Previous studies investigated the contributing factors that affect gravel roads crash severities. There were widely range of contributory factors such as road conditions, weather conditions, vehicle type, driver behavior and characteristics, road geometric features, terrain, and environmental conditions that were investigated. However, no previous studies accounted for unobserved heterogeneity in the crash data while modeling gravel roads crash severities. This study employed random-parameter binary logit model also known as mixed logit model to model severity of gravel road crashes in Wyoming. Gravel roads crash data from Wyoming Department of Transportation from 2010 to 2019 were studied and analyzed. Both binary logistic regression model and mixed binary logistic regression model were developed. Mixed logistic binary regression model was found to be better fit the data in terms of goodness of fit statistics. The results showed that crashes involving motorcycles had the higher risk of fatalities and serious injuries. In addition, horizontal curves and male drivers were found to have significant effect on the gravel roads crash severities. Passenger cars were found as a random parameter from the results of mixed binary logistic regression model. In addition, existence of horizontal curve was found to be the most contributing factors increase the probability of having minor or possible injuries in gravel roads accidents. Furthermore, having a snow on the road surface was found to have significant effect on increasing the probability of resulting in property damage only accidents. The results found in this study will be helpful to identify effective countermeasures which will reduce gravel road crash severity. Thus, applied variable speed limits at the horizontal curves location and at snow falling time will enhance the gravel roads safety.

Authors

Publication Details

Journal
Emirati Journal of Civil Engineering and Applications
Published
2024-11-28
DOI
https://doi.org/10.54878/xy70mf85
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Severities of Gravel Road Crashes Using Random Parameters

Khaled Ksaibati, Uttara Roy, Osama Abu Daoud
Emirati Journal of Civil Engineering and Applications
Landslides and related hazards
article

Modeling Severities of Gravel Road Crashes Using Random Parameters

Khaled Ksaibati, Uttara Roy, Osama Abu Daoud
article en

Abstract

Gravel roads crashes are a roadway safety hazard and such crashes have higher probability of fatalities and serious injuries when compared to paved roads crashes. Previous studies investigated the contributing factors that affect gravel roads crash severities. There were widely range of contributory factors such as road conditions, weather conditions, vehicle type, driver behavior and characteristics, road geometric features, terrain, and environmental conditions that were investigated. However, no previous studies accounted for unobserved heterogeneity in the crash data while modeling gravel roads crash severities. This study employed random-parameter binary logit model also known as mixed logit model to model severity of gravel road crashes in Wyoming. Gravel roads crash data from Wyoming Department of Transportation from 2010 to 2019 were studied and analyzed. Both binary logistic regression model and mixed binary logistic regression model were developed. Mixed logistic binary regression model was found to be better fit the data in terms of goodness of fit statistics. The results showed that crashes involving motorcycles had the higher risk of fatalities and serious injuries. In addition, horizontal curves and male drivers were found to have significant effect on the gravel roads crash severities. Passenger cars were found as a random parameter from the results of mixed binary logistic regression model. In addition, existence of horizontal curve was found to be the most contributing factors increase the probability of having minor or possible injuries in gravel roads accidents. Furthermore, having a snow on the road surface was found to have significant effect on increasing the probability of resulting in property damage only accidents. The results found in this study will be helpful to identify effective countermeasures which will reduce gravel road crash severity. Thus, applied variable speed limits at the horizontal curves location and at snow falling time will enhance the gravel roads safety.

Emirati Journal of Civil Engineering and ApplicationsVol. 2(2)
Sustainable cities and communities
Openalex Percentile: Top 68%
Landslides and related hazards
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Modeling Severities of Gravel Road Crashes Using Random Parameters — Khaled Ksaibati, Uttara Roy, et al. · Emirati Journal of Civil Engineering and Applications (2024) | TGRS Research Map | TGRS