AIMC Topic: Accidents, Traffic

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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 Accidents, Traffic Artificial Intelligence Transportation Humans
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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...
Deep Learning Seasons Railroads 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...
Humans Artificial Intelligence Pedestrians Accidents, Traffic Automobiles Walking Safety
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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...
Support Vector Machine Accidents, Traffic Humans Research Design
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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...
Research Design Accidents, Traffic Humans Neural Networks, Computer
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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 Machine Learning Risk Assessment Accidents, Traffic Deep Learning
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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...
Young Adult Motorcycles Adult Accidents, Traffic Bicycling Humans Queensland Machine Learning Support Vector Machine Male Female Pedestrians Middle Aged
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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 Accidents, Traffic Automobile Driving Computer Simulation Cluster Analysis
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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 ...
Neural Networks, Computer Accidents, Traffic Distracted Driving
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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...
Cluster Analysis Accidents, Traffic Deep Learning Logistic Models Neural Networks, Computer Humans
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