AIMC Topic: Accidents, Traffic

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

Accident; analysis and prevention Feb 21, 2022
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...
Risk Factors Accidents, Traffic Humans Machine Learning
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Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents.

PloS one Feb 2, 2022
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...
Area Under Curve Bayes Theorem ROC Curve Logistic Models Wounds and Injuries Accidents, Traffic Pakistan Humans Machine Learning Severity of Illness Index
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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...
Recognition, Psychology Accidents, Traffic Automobile Driving Neural Networks, Computer Deep Learning Humans
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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...
Humans Accidents, Traffic Attention Risk-Taking Neural Networks, Computer Distracted Driving Models, Biological
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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...
Bicycling Transportation Accidents, Traffic Electricity Humans Artificial Intelligence
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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 Humans Probability Decision Making Automobile Driving
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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...
Accidents, Traffic Computer Simulation Cities Neural Networks, Computer Algorithms Humans
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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...
Artificial Intelligence Machine Learning Mobile Applications Accidents, Traffic Humans 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 Humans Machine Learning Algorithms Accidents, Traffic
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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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