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Accidents, Traffic

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An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors.

Accident; analysis and prevention
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 and further application in developing countermeasures to g...

Crash severity analysis of vulnerable road users using machine learning.

PloS one
Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on empl...

Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

PloS one
The classification of driving styles plays a fundamental role in evaluating drivers' driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the mo...

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification.

Sensors (Basel, Switzerland)
Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent ...

Understand the impact of traffic states on crash risk in the vicinities of Type A weaving segments: A deep learning approach.

Accident; analysis and prevention
The primary objective of this study was to evaluate the impacts of traffic states on crash risk in the vicinities of Type A weaving segments. A deep convolutional embedded clustering (DCEC) was developed to classify traffic flow into nine states. The...

Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling.

Accident; analysis and prevention
Crash data analysis is commonly subjected to imbalanced data. Varied by facility and control types, some crash types are more frequent than others. However, uncommon crash types are routinely more severe and associated with higher economic and societ...

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study.

International journal of injury control and safety promotion
A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlati...

Identifying factors associated with roadside work zone collisions using machine learning techniques.

Accident; analysis and prevention
Identifying factors that are associated with the probability of roadside work zone collisions enables decision makers to better assess and control the risk of scheduling a particular maintenance or construction activity by modifying the characteristi...

Cost-sensitive learning for semi-supervised hit-and-run analysis.

Accident; analysis and prevention
Hit-and-run crashes not only degrade the morality, but also result in delays of medical services provided to victims. However, class imbalance problem exists as the number of hit-and-run crashes is much smaller than that of non-hit-and-run crashes. T...

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Sensors (Basel, Switzerland)
Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose...