AIMC Topic: Accidents, Traffic

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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 Jan 27, 2022
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...
Neural Networks, Computer Deep Learning Humans Recognition, Psychology Accidents, Traffic Automobile Driving
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A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data.

PloS one Jan 25, 2022
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...
Risk-Taking Distracted Driving Neural Networks, Computer Models, Biological Accidents, Traffic Attention Humans
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Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review.

Sensors (Basel, Switzerland) Jan 8, 2022
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...
Humans Artificial Intelligence Bicycling Accidents, Traffic Transportation Electricity
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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 Dec 29, 2021
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...
Accidents, Traffic Logistic Models Probability Automobile Driving Humans Decision Making
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Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking.

Sensors (Basel, Switzerland) Dec 29, 2021
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...
Humans Neural Networks, Computer Algorithms Computer Simulation Cities Accidents, Traffic
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Understanding the potential of emerging digital technologies for improving road safety.

Accident; analysis and prevention Dec 28, 2021
Each year, 1.35 million people are killed on the world's roads and another 20-50 million are seriously injured. Morbidity or serious injury from road traffic collisions is estimated to increase to 265 million people between 2015 and 2030. Current roa...
Machine Learning Humans Artificial Intelligence Mobile Applications Accidents, Traffic Digital Technology
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A literature review of machine learning algorithms for crash injury severity prediction.

Journal of safety research Dec 23, 2021
INTRODUCTION: Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining suc...
Support Vector Machine Bayes Theorem Machine Learning Accidents, Traffic Algorithms Humans
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Transfer learning for spatio-temporal transferability of real-time crash prediction models.

Accident; analysis and prevention Dec 8, 2021
Real-time crash prediction is a heavily studied area given their potential applications in proactive traffic safety management in which a plethora of statistical and machine learning (ML) models have been developed to predict traffic crashes in real-...
Forecasting Accidents, Traffic Safety Management Neural Networks, Computer Machine Learning Humans
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Variance-based global sensitivity analysis for rear-end crash investigation using deep learning.

Accident; analysis and prevention Dec 8, 2021
Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limit...
Logistic Models Models, Statistical Machine Learning Deep Learning Accidents, Traffic Humans
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Finding and understanding pedal misapplication crashes using a deep learning natural language model.

Traffic injury prevention Dec 7, 2021
OBJECTIVE: The objective of this study was to develop a system which used the BERT natural language understanding model to identify pedal misapplication (PM) crashes from their crash narratives and validate the accuracy of the system.
Accidents, Traffic North Carolina Deep Learning Algorithms Humans Language
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