A literature review of machine learning algorithms for crash injury severity prediction
Introduction: Road traffic crashes represent a major public health concern, so it is of
significant importance to understand the factors associated with the increase of injury …
significant importance to understand the factors associated with the increase of injury …
[HTML][HTML] Machine learning applied to road safety modeling: A systematic literature review
Road safety modeling is a valuable strategy for promoting safe mobility, enabling the
development of crash prediction models (CPM) and the investigation of factors contributing …
development of crash prediction models (CPM) and the investigation of factors contributing …
An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors
Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment.
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …
Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree
X Zhou, P Lu, Z Zheng, D Tolliver, A Keramati - Reliability Engineering & …, 2020 - Elsevier
Safety is a major concern of transportation planners and engineers in their design of
highway rail grade crossings (HRGCs). Safety agencies rely on prediction models to …
highway rail grade crossings (HRGCs). Safety agencies rely on prediction models to …
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 …
Crash injury severity analysis using a two-layer Stacking framework
Crash injury severity analysis is useful for traffic management agency to further understand
severity of crashes. A two-layer Stacking framework is proposed in this study to predict the …
severity of crashes. A two-layer Stacking framework is proposed in this study to predict the …
Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model
A Hosseinzadeh, A Moeinaddini… - Journal of safety …, 2021 - Elsevier
Introduction: Reducing the severity of crashes is a top priority for safety researchers due to
its impact on saving human lives. Because of safety concerns posed by large trucks and the …
its impact on saving human lives. Because of safety concerns posed by large trucks and the …
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 …
Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors
This study develops a Bayesian spatial generalized ordered logit model with conditional
autoregressive priors to examine severity of freeway crashes. Our model can simultaneously …
autoregressive priors to examine severity of freeway crashes. Our model can simultaneously …
Traffic accident's severity prediction: A deep-learning approach-based CNN network
M Zheng, T Li, R Zhu, J Chen, Z Ma, M Tang… - IEEE …, 2019 - ieeexplore.ieee.org
In traffic accident, an accurate and timely severity prediction method is necessary for the
successful deployment of an intelligent transportation system to provide corresponding …
successful deployment of an intelligent transportation system to provide corresponding …