AIMC Topic: Accidents, Traffic

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Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach.

International journal of environmental research and public health Oct 8, 2021
BACKGROUND: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strat...
Neural Networks, Computer Machine Learning Logistic Models Accidents, Traffic Algorithms Cross-Sectional Studies
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A deep learning approach for real-time crash prediction using vehicle-by-vehicle data.

Accident; analysis and prevention Sep 30, 2021
In road safety, real-time crash prediction may play a crucial role in preventing such traffic events. However, much of the research in this line generally uses data aggregated every five or ten minutes. This article proposes a new image-inspired data...
Accidents, Traffic Deep Learning Chile Humans Neural Networks, Computer
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Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.

Computational intelligence and neuroscience Sep 29, 2021
Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairin...
Deep Learning Neural Networks, Computer Transportation Humans Accidents, Traffic Artificial Intelligence
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Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning.

Sensors (Basel, Switzerland) Sep 18, 2021
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensor...
Seasons Railroads Deep Learning Accidents, Traffic Weather
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Investigating yielding behavior of heterogeneous vehicles at a semi-controlled crosswalk.

Accident; analysis and prevention Aug 31, 2021
It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to...
Pedestrians Accidents, Traffic Safety Automobiles Walking Artificial Intelligence Humans
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A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models.

Accident; analysis and prevention Aug 31, 2021
An innovative approach for real-time road safety analysis is presented in this work. Unlike traditional real-time crash prediction models (RTCPMs), in which crash data are used in the training phase, a real-time conflict prediction model (RTConfPM) i...
Accidents, Traffic Research Design Support Vector Machine Humans
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Traffic Accident Data Generation Based on Improved Generative Adversarial Networks.

Sensors (Basel, Switzerland) Aug 27, 2021
For urban traffic, traffic accidents are the most direct and serious risk to people's lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronte...
Neural Networks, Computer Research Design Accidents, Traffic Humans
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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 Aug 5, 2021
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...
Humans Deep Learning Accidents, Traffic Machine Learning Risk Assessment
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Crash severity analysis of vulnerable road users using machine learning.

PloS one Aug 5, 2021
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...
Queensland Young Adult Motorcycles Adult Humans Support Vector Machine Accidents, Traffic Male Machine Learning Pedestrians Bicycling Middle Aged Female
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Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

PloS one Jul 19, 2021
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...
Humans Machine Learning Algorithms Computer Simulation Automobile Driving Cluster Analysis Accidents, Traffic
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