AIMC Topic: Accidents, Traffic

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Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.

International journal of environmental research and public health Jul 18, 2020
Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pe...
Risk Assessment Geographic Information Systems China Humans Machine Learning Automobile Driving Spatial Analysis Pedestrians Accidents, Traffic
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Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.

Accident; analysis and prevention Jul 16, 2020
Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicl...
Risk Michigan Humans Neural Networks, Computer Accidents, Traffic Reproducibility of Results Data Collection Technology Deep Learning Automobiles
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The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms.

Accident; analysis and prevention Jul 2, 2020
In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to...
Humans Injury Severity Score Built Environment Bicycling Fuzzy Logic Accidents, Traffic Sex Factors
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Hybrid SVM-CNN Classification Technique for Human-Vehicle Targets in an Automotive LFMCW Radar.

Sensors (Basel, Switzerland) Jun 21, 2020
Human-vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-...
Motor Vehicles Accidents, Traffic Radar Humans Neural Networks, Computer Algorithms Support Vector Machine Pedestrians
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Automated traffic incident detection with a smaller dataset based on generative adversarial networks.

Accident; analysis and prevention Jun 20, 2020
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 novel incident detection framework is proposed based on generative ad...
Support Vector Machine Sample Size Accidents, Traffic Built Environment Humans Research Design
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Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach.

International journal of environmental research and public health Jun 9, 2020
Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning...
Databases, Factual Risk Factors Accidents, Traffic Machine Learning Data Analysis Wounds and Injuries Humans
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Forecasting deaths of road traffic injuries in China using an artificial neural network.

Traffic injury prevention Jun 5, 2020
This study was conducted to estimate road traffic deaths and to forecast short-term road traffic deaths in China using the Elman recurrent neural network (ERNN) model. An ERNN model was developed using reported police data of road traffic deaths in ...
Accidents, Traffic Forecasting Wounds and Injuries Humans Reproducibility of Results Neural Networks, Computer China
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Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data.

Accident; analysis and prevention May 11, 2020
Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will p...
Automobile Driving Humans Weather Deep Learning Videotape Recording Data Collection Accidents, Traffic
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Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Accident; analysis and prevention May 11, 2020
Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks...
Humans Weather Automobile Driving Data Accuracy Machine Learning Databases, Factual Accidents, Traffic
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A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt.

International journal of injury control and safety promotion Apr 1, 2020
The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequentl...
Adult Male Middle Aged Adolescent Triage Databases, Factual Female Occupational Injuries Quality Improvement Young Adult Wounds and Injuries Egypt Humans Trauma Severity Indices Accidents, Traffic Machine Learning
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