AIMC Topic: Accidents, Traffic

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

Sensors (Basel, Switzerland) Jul 15, 2021
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 ...
Accidents, Traffic Distracted Driving Neural Networks, Computer
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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 Jul 9, 2021
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...
Humans Neural Networks, Computer Cluster Analysis Accidents, Traffic Deep Learning Logistic Models
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Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling.

Accident; analysis and prevention Jun 16, 2021
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...
Adaptation, Psychological Accidents, Traffic Humans Machine Learning Models, Statistical
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Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study.

International journal of injury control and safety promotion Jun 1, 2021
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...
Machine Learning Logistic Models Algorithms Models, Statistical Humans Accidents, Traffic
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Identifying factors associated with roadside work zone collisions using machine learning techniques.

Accident; analysis and prevention Jun 1, 2021
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...
Humans Probability Police Machine Learning Accidents, Traffic
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Cost-sensitive learning for semi-supervised hit-and-run analysis.

Accident; analysis and prevention May 25, 2021
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...
Probability Australia Accidents, Traffic Logistic Models Humans Machine Learning
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Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Sensors (Basel, Switzerland) Mar 30, 2021
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...
Neural Networks, Computer Galvanic Skin Response Heart Rate Accidents, Traffic Automobile Driving
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A deep learning based traffic crash severity prediction framework.

Accident; analysis and prevention Mar 16, 2021
Highway work zones are most vulnerable roadway segments for congestion and traffic collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions at work zones is vital to reduce the response time for emergency unit...
Humans Algorithms Accidents, Traffic Deep Learning Louisiana Machine Learning
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Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research.

Accident; analysis and prevention Feb 8, 2021
Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study i...
Research Reproducibility of Results Humans Safety Automobile Driving Robotics Accidents, Traffic
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Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles.

Accident; analysis and prevention Feb 5, 2021
The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV's market penetration increases, the interacti...
Texas Time Factors Robotics Automobile Driving Accidents, Traffic Humans Acceleration Safety
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