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 …
[HTML][HTML] Enhancing construction safety: Machine learning-based classification of injury types
The construction industry is a hazardous industry with significant injuries and fatalities. Few
studies have used data-driven analysis to investigate injuries due to construction accidents …
studies have used data-driven analysis to investigate injuries due to construction accidents …
Handling imbalanced data in road crash severity prediction by machine learning algorithms
N Fiorentini, M Losa - Infrastructures, 2020 - mdpi.com
Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine
learning algorithms for predicting crash severity have recently gained interest by the …
learning algorithms for predicting crash severity have recently gained interest by the …
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 …
Automated traffic incident detection with a smaller dataset based on generative adversarial networks
An imbalanced and small training sample can cause an incident detection model to have a
low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a …
low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a …
Comparison of machine learning algorithms for predicting traffic accident severity
RE AlMamlook, KM Kwayu… - 2019 IEEE Jordan …, 2019 - ieeexplore.ieee.org
Traffic accidents are among the most critical issues facing the world as they cause many
deaths, injuries, and fatalities as well as economic losses every year. Accurate models to …
deaths, injuries, and fatalities as well as economic losses every year. Accurate models to …
Comparing machine learning and deep learning methods for real-time crash prediction
A Theofilatos, C Chen… - Transportation research …, 2019 - journals.sagepub.com
Although there are numerous studies examining the impact of real-time traffic and weather
parameters on crash occurrence on freeways, to the best of the authors' knowledge there …
parameters on crash occurrence on freeways, to the best of the authors' knowledge there …
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) …
[HTML][HTML] Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers
JPSS Madushani, RMK Sandamal… - Transportation …, 2023 - Elsevier
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%)
due to the expansion of the transport network and high traffic volume. It is crucial to identify …
due to the expansion of the transport network and high traffic volume. It is crucial to identify …
Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data
This study aims to classify the injury severity in motor-vehicle crashes with both high
accuracy and sensitivity rates. The dataset used in this study contains 297,113 vehicle …
accuracy and sensitivity rates. The dataset used in this study contains 297,113 vehicle …