[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 …
Applications of machine learning methods in traffic crash severity modelling: current status and future directions
As a key area of traffic safety research, crash severity modelling has attracted tremendous
attention. Recently, there has been growing interest in applying machine learning (ML) …
attention. Recently, there has been growing interest in applying machine learning (ML) …
Visualization and analysis of mapping knowledge domain of road safety studies
X Zou, WL Yue, H Le Vu - Accident Analysis & Prevention, 2018 - Elsevier
Mapping knowledge domain (MKD) is an important application of visualization technology in
Bibliometrics, which has been extensively applied in psychology, medicine, and information …
Bibliometrics, which has been extensively applied in psychology, medicine, and information …
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 …
[HTML][HTML] A classification and regression tree algorithm for heart disease modeling and prediction
Heart disease remains the leading cause of death, such that nearly one-third of all deaths
worldwide are estimated to be caused by heart-related conditions. Advancing applications of …
worldwide are estimated to be caused by heart-related conditions. Advancing applications of …
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 …
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 …
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 …
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 …
Analysis of traffic accident severity using decision rules via decision trees
A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its
main advantages is that Decision Rules (DRs) can be extracted from its structure. And these …
main advantages is that Decision Rules (DRs) can be extracted from its structure. And these …