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Accidents, Traffic

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Investigating the effect of road condition and vacation on crash severity using machine learning algorithms.

International journal of injury control and safety promotion
Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial an...

The usefulness of artificial intelligence for safety assessment of different transport modes.

Accident; analysis and prevention
Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Inte...

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies.

Traffic injury prevention
OBJECTIVE: The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomo...

Deep Learning with Attention Mechanisms for Road Weather Detection.

Sensors (Basel, Switzerland)
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assi...

How Do Autonomous Vehicles Decide?

Sensors (Basel, Switzerland)
The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in t...

Unusual Driver Behavior Detection in Videos Using Deep Learning Models.

Sensors (Basel, Switzerland)
Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to m...

Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios.

Sensors (Basel, Switzerland)
In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of...

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Accident; analysis and prevention
Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving beha...

Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images.

International journal of environmental research and public health
In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becomi...

Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network.

Computational intelligence and neuroscience
The popularity of private cars has brought great convenience to citizens' travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increas...