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Accidents, Traffic

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Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach.

International journal of environmental research and public health
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver as...

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model.

Sensors (Basel, Switzerland)
The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accide...

On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development.

Accident; analysis and prevention
Machine learning (ML) model interpretability has attracted much attention recently given the promising performance of ML methods in crash frequency studies. Extracting accurate relationship between risk factors and crash frequency is important for un...

Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents.

PloS one
To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machi...

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines.

International journal of environmental research and public health
The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some ex...

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data.

PloS one
Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensor...

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review.

Sensors (Basel, Switzerland)
This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider's physical and physiological information, monitoring of their health state, and adjusting the "medical" assistance accord...

Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset.

Journal of safety research
INTRODUCTION: Driver behavior related to overtaking maneuvers, which are considered a major safety risk determinant on two-lane two-way highway in low- and middle-income countries (LMIC), are an important subject of further analysis. This study evalu...

Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking.

Sensors (Basel, Switzerland)
The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Althoug...