Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Liquid crystal biosensing platform based on whispering gallery mode laser for Aβ42 detection.

Colloids and surfaces. B, Biointerfaces·2026

Same author

A universal 6iL/E4 culture system for deriving and maintaining embryonic stem cells across mammalian species.

Cell research·2026

Same author

Historical roots of hospital-centred operational governance in China: a path dependence analysis and its implications for global health system reform.

BMJ global health·2026

Same author

Environmental Health Literacy Prevalence and Profiles among Shanghai Residents - Shanghai, China, 2020-2024.

China CDC weekly·2026

Same author

Evaluating deep coal rock gas fracturing sweet spot intervals using PSO-ELM algorithm and petrophysical logging data.

Scientific reports·2026

Same author

Association of body mass index and serum markers with immune-related adverse events in lung cancer patients receiving immune checkpoint inhibitors.

Journal of thoracic disease·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An

Zhiyuan Sun¹, Zhoumeng Ai¹, Zehao Wang²

¹Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.

Accident; Analysis and Prevention

|August 14, 2024

Summary

This study reveals key factors behind serious traffic violations by elderly drivers, using an interpretable machine learning framework. Built environment attributes significantly influence ordinary and severe violations, with distinct patterns emerging for each severity level.

Keywords:

Elderly drivers Interpretable machine learning framework Multi-scale built environment Multi-source data Traffic violations

More Related Videos

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jun 17, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Traffic safety research
Machine learning applications
Gerontology

Background:

Elderly drivers exhibit unique traffic violation patterns compared to other age groups.
Serious traffic violations by elderly drivers contribute to severe traffic crashes.
Understanding these patterns is crucial for developing targeted safety interventions.

Purpose of the Study:

To explore patterns of ordinary and severe traffic violations among elderly drivers.
To develop and evaluate an interpretable machine learning framework for analyzing these violations.
To identify key influencing factors, particularly built environment attributes.

Main Methods:

Categorized traffic violation severity into slight, ordinary, and severe levels based on point deduction.
Employed an interpretable machine learning framework with four enhanced models.
Utilized adaptive synthetic sampling for imbalanced data, multi-objective feature selection (NSGA-II), and Bayesian hyperparameter optimization.

Main Results:

The interpretable machine learning framework significantly improved model performance and distinguishability.
Six of the top ten identified factors influencing violations were related to multi-scale built environment attributes.
Identified shared and unique influencing factors and interaction effects for ordinary and severe violations.

Conclusions:

The interpretable machine learning framework effectively analyzes elderly driver violations.
Built environment characteristics are critical determinants of serious traffic violations in this demographic.
Distinct yet overlapping patterns exist between ordinary and severe violations, necessitating nuanced safety strategies.