AIMC Topic: Accidents, Traffic

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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...
Humans Adaptation, Psychological Models, Statistical Accidents, Traffic Machine Learning
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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...
Humans Machine Learning Models, Statistical Accidents, Traffic Algorithms Logistic Models
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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 Machine Learning Accidents, Traffic Probability Police
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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...
Machine Learning Logistic Models Humans Probability Australia Accidents, Traffic
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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...
Galvanic Skin Response Neural Networks, Computer Accidents, Traffic Heart Rate 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...
Algorithms Deep Learning Accidents, Traffic Louisiana Humans 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 Safety Humans Robotics Accidents, Traffic Automobile Driving
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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...
Accidents, Traffic Safety Automobile Driving Acceleration Time Factors Robotics Humans Texas
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Predicting and Interpreting Spatial Accidents through MDLSTM.

International journal of environmental research and public health Feb 3, 2021
Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear...
China Neural Networks, Computer Accidents, Traffic
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Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

PloS one Feb 3, 2021
With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not a...
Accidents, Traffic Kenya Machine Learning City Planning Humans Social Media Motorcycles
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