Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 15, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Utilizing support vector machine in real-time crash risk evaluation.

Rongjie Yu1, Mohamed Abdel-Aty

  • 1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32826-2450, United States. rongjie.yu@knights.ucf.edu

Accident; Analysis and Prevention
|January 5, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

You might also read

Related Articles

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

Sort by
Same author

Crash root-cause identification via trace-rewarded causation chain reasoning large language model.

Accident; analysis and prevention·2026
Same author

Comparative safety evaluation of ADAS-Equipped electric and gasoline vehicles using real-world crash data.

Accident; analysis and prevention·2026
Same author

Novel small-molecule positive allosteric modulator 1 with blood-brain barrier penetration activity exerts anti-cellular senescence effects via the PAC1-R/YY1/SIRT6 pathway.

Acta biochimica et biophysica Sinica·2026
Same author

When does visual distraction become dangerous in car-following? Evidence from naturalistic driving study data with causal inference on time-to-collision and braking intensity.

Accident; analysis and prevention·2026
Same author

Determinants influencing risks in e-bike cyclists under mix traffic condition: a partially constrained random parameters approach using experimental study data.

Accident; analysis and prevention·2025
Same author

Segment level safety analysis using lane-changing behavior and driving volatility features from connected vehicle trajectories.

Scientific reports·2025
Same journal

Modeling road-segment-level speeding risk of new energy vehicle taxis using a multistage framework with spatial spillover, endogeneity, and nonlinear effects.

Accident; analysis and prevention·2026
Same journal

Role of streetscape feature in pedestrian safety: A modified multi-level multiple membership model.

Accident; analysis and prevention·2026
Same journal

Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.

Accident; analysis and prevention·2026
Same journal

Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

Accident; analysis and prevention·2026
Same journal

Differential sensitivity of self-reported driving and collision measures to aspects of shiftwork, sleep, and fatigue.

Accident; analysis and prevention·2026
Same journal

Delving into the visual attention of pedestrians during street crossing under time pressure: An eye-tracking approach.

Accident; analysis and prevention·2026
See all related articles

Support Vector Machine (SVM) models show superior real-time crash risk prediction compared to traditional methods. SVM with a Radial-basis kernel function offers enhanced accuracy for active traffic management safety improvements.

Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Machine Learning Applications

Background:

  • Real-time crash risk evaluation is crucial for Active Traffic Management (ATM).
  • Previous models like logistic regression and neural networks have limitations in functional form and over-fitting.
  • Existing studies primarily focused on model estimation rather than predictive performance.

Purpose of the Study:

  • To introduce and evaluate Support Vector Machine (SVM) for real-time crash risk assessment.
  • To compare SVM performance against Bayesian logistic regression models.
  • To investigate factors influencing SVM predictive capability in traffic safety.

Main Methods:

  • Utilized Classification and Regression Trees (CART) for variable selection.

Related Experiment Videos

Last Updated: May 15, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Developed and compared Bayesian logistic regression models with varying heterogeneity.
  • Implemented and assessed SVM models with different kernel functions, including Radial-basis kernel.
  • Performed extension analyses on sample size, variable selection, and variable effects.

    • Main Results:

    • The SVM model with a Radial-basis kernel function demonstrated superior predictive performance based on Area Under the ROC Curve (AUC).
    • Smaller sample sizes were found to enhance SVM classification accuracy.
    • Variable selection prior to SVM estimation is essential for optimal performance.
    • Explanatory variables had similar effects on crash occurrence in both SVM and logistic regression models.

    Conclusions:

    • SVM models, particularly with a Radial-basis kernel, are effective for real-time crash risk evaluation in ATM.
    • The findings highlight the importance of variable selection and sample size considerations for SVM implementation.
    • SVM offers a promising alternative to traditional models for improving traffic safety prediction.