Injury severity prediction of traffic crashes with ensemble machine learning techniques: A comparative study
A better understanding of injury severity risk factors is fundamental to improving crash
prediction and effective implementation of appropriate mitigation strategies. Traditional …
prediction and effective implementation of appropriate mitigation strategies. Traditional …
Transparent deep machine learning framework for predicting traffic crash severity
Abstract Analysis of crash injury severity is a promising research target in highway safety
studies. A better understanding of crash severity risk factors is vital for the proactive …
studies. A better understanding of crash severity risk factors is vital for the proactive …
Older pedestrian traffic crashes severity analysis based on an emerging machine learning XGBoost
M Guo, Z Yuan, B Janson, Y Peng, Y Yang, W Wang - Sustainability, 2021 - mdpi.com
Older pedestrians are vulnerable on the streets and at significant risk of injury or death when
involved in crashes. Pedestrians' safety is critical for roadway agencies to consider and …
involved in crashes. Pedestrians' safety is critical for roadway agencies to consider and …
Temporal stability of the impact of factors determining drivers' injury severities across traffic barrier crashes in mountainous regions
Traffic barrier crashes have been a major concern in many prior studies in traffic safety
literature, especially in the crash-prone sections of mountainous regions. However, the effect …
literature, especially in the crash-prone sections of mountainous regions. However, the effect …
Exploring factors affecting the injury severity of freeway tunnel crashes: A random parameters approach with heterogeneity in means and variances
Generally, freeway tunnels are built to overcome the complex driving environments in
mountainous terrains. However, crashes in these tunnels can be more severe than those on …
mountainous terrains. However, crashes in these tunnels can be more severe than those on …
Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity
The latent class clustering and latent segmentation-based models are employed to account
for heterogeneity across different groups. Further, the random parameter variants of these …
for heterogeneity across different groups. Further, the random parameter variants of these …
Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm
Pedestrians and bicyclists from marginalized and underserved populations experienced
disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity …
disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity …
Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models
Due to limited visibility and low skid resistance on road surface, single-vehicle crashes
under rain conditions, especially those occurred in rural areas, are more likely to result in …
under rain conditions, especially those occurred in rural areas, are more likely to result in …
Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes
The single-vehicle crash has been recognized as a critical crash type due to its high fatality
rate. In this study, a two-year crash dataset including all single-vehicle crashes in New …
rate. In this study, a two-year crash dataset including all single-vehicle crashes in New …
Road accident analysis with data mining approach: evidence from Rome
Nowadays, road accident is one of the main causes of mortality worldwide. Then, measures
are required to reduce or mitigate the accident impacts. The identification of the most …
are required to reduce or mitigate the accident impacts. The identification of the most …