Data science and big data analytics: A systematic review of methodologies used in the supply chain and logistics research
Data science and big data analytics (DS &BDA) methodologies and tools are used
extensively in supply chains and logistics (SC &L). However, the existing insights are …
extensively in supply chains and logistics (SC &L). However, the existing insights are …
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 …
Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP
Understanding and quantifying the effects of risk factors on crash frequency is of great
importance for developing cost-effective safety countermeasures. In this paper, the effects of …
importance for developing cost-effective safety countermeasures. In this paper, the effects of …
The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
Due to the burgeoning demand for freight movement, freight related road safety threats have
been growing substantially. In spite of some research on the factors influencing freight truck …
been growing substantially. In spite of some research on the factors influencing freight truck …
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] Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and developing effective road safety …
are a prerequisite for devising countermeasures and developing effective road safety …
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 …
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 …
[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 …
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 …