AIMC Topic: Accidents, Traffic

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A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model.

Accident; analysis and prevention Sep 16, 2024
To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley ...
London Bicycling Support Vector Machine Models, Statistical Regression Analysis Bayes Theorem Random Forest Accidents, Traffic Machine Learning Algorithms Risk Factors Humans
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Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models.

Accident; analysis and prevention Aug 29, 2024
A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-en...
Humans Machine Learning Decision Support Techniques China Biomechanical Phenomena Bicycling Accidents, Traffic Models, Theoretical Risk Assessment Equipment Design Safety Wounds and Injuries
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Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

Accident; analysis and prevention Aug 24, 2024
The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to ...
Automobile Driving Linear Models Risk-Taking Deep Learning Accidents, Traffic Humans Neural Networks, Computer
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Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning.

Frontiers in public health Aug 22, 2024
INTRODUCTION: Enhancing road safety conditions alleviates socioeconomic hazards from traffic accidents and promotes public health. Monitoring progress and recalibrating measures are indispensable in this effort. A systematic and scientific decision-m...
Accidents, Traffic Safety Humans Algorithms Machine Learning Reproducibility of Results Cluster Analysis
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Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.

Accident; analysis and prevention Aug 13, 2024
The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into t...
Automobile Driving Built Environment Humans Machine Learning Aged Aged, 80 and over Bayes Theorem Accidents, Traffic
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Analysis of harsh braking and harsh acceleration occurrence via explainable imbalanced machine learning using high-resolution smartphone telematics and traffic data.

Accident; analysis and prevention Aug 8, 2024
Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not bee...
Machine Learning Algorithms Female Humans Accidents, Traffic Automobile Driving Acceleration Adult Male Smartphone Greece
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Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

Ergonomics Aug 7, 2024
This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected ...
Female Young Adult Adult Humans Reaction Time Accidents, Traffic Automobile Driving Male Time Factors Automobiles
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Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models.

Accident; analysis and prevention Jul 6, 2024
Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both unc...
Neural Networks, Computer Accidents, Traffic Support Vector Machine Humans Wounds and Injuries Models, Statistical Trauma Severity Indices
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An Identification Method for Road Hypnosis Based on Human EEG Data.

Sensors (Basel, Switzerland) Jul 6, 2024
The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and canno...
Humans Automobile Driving Neural Networks, Computer Electroencephalography Hypnosis Accidents, Traffic
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Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.

Accident; analysis and prevention Jul 4, 2024
Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is ess...
Accidents, Traffic Automobile Driving Spatial Analysis Wounds and Injuries Male Middle Aged Models, Statistical Risk Factors Adult Humans Machine Learning Female
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