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 Concept Videos

Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

The behavior of elastoplastic materials under bending stresses, particularly in structural members with rectangular cross-sections, is crucial for predicting material responses and understanding failure modes. Initially, when a bending moment is applied, the stress distribution across the section follows Hooke's Law and is linear and elastic. This distribution means the stress increases from the neutral axis to the maximum at the outer fibers, up to the elastic limit.
As the bending moment...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

You might also read

Related Articles

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

Sort by
Same author

Remimazolam for sedation in flexible bronchoscopy: A systematic review and meta-analysis of randomised controlled trials.

Pulmonology·2026
Same author

ICU Admission and Post-Discharge Mortality in COVID-19: Different Risk Factors Across Clinical Phases.

Medical sciences (Basel, Switzerland)·2026
Same author

Evaluation of the Tunnelling Technique for Upper Labial Frenectomy Using ER:YAG Laser.

Journal of clinical and experimental dentistry·2026
Same author

Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.

Journal of clinical medicine·2026
Same author

Recovering Historical eDNA From Museum-Preserved Filter Feeders via Non-Destructive Metabarcoding.

Molecular ecology resources·2026
Same author

Admission of Children from Portuguese-Speaking African Countries to a Portuguese Early Childhood Medical Unit.

Acta medica portuguesa·2026

Related Experiment Video

Updated: May 30, 2026

A Contusive Model of Unilateral Cervical Spinal Cord Injury Using the Infinite Horizon Impactor
07:28

A Contusive Model of Unilateral Cervical Spinal Cord Injury Using the Infinite Horizon Impactor

Published on: July 24, 2012

A note on modeling road accident frequency: a flexible elasticity model.

António Couto1, Sara Ferreira1

  • 1Civil Engineering Department, School of Engineering, Porto University, Rua Dr. Roberto Frias 4200-465 Porto, Portugal.

Accident; Analysis and Prevention
|August 9, 2011
PubMed
Summary

This study introduces the translog function for accident modeling, offering a more flexible approach than traditional log-linear models. Results show improved goodness-of-fit and predictive performance in accident frequency analysis.

More Related Videos

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Related Experiment Videos

Last Updated: May 30, 2026

A Contusive Model of Unilateral Cervical Spinal Cord Injury Using the Infinite Horizon Impactor
07:28

A Contusive Model of Unilateral Cervical Spinal Cord Injury Using the Infinite Horizon Impactor

Published on: July 24, 2012

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Area of Science:

  • Transportation Science
  • Econometrics
  • Statistical Modeling

Background:

  • Count data models are standard for accident modeling, often using log-linear functions.
  • Log-linear functions assume constant elasticity, limiting analysis of explanatory variable effects on accident risk.
  • Existing road safety models rarely explore flexible function forms for non-constant elasticity.

Purpose of the Study:

  • To evaluate the translog function, typically used in economics, for accident modeling.
  • To compare the translog function's performance against the traditional log-linear negative binomial (NB) model.
  • To assess the potential of a flexible function form for accident frequency analysis.

Main Methods:

  • Applied the translog function to accident data, allowing elasticity to vary with explanatory variables.
  • Compared the translog-enhanced NB model with the traditional NB model using goodness-of-fit statistics and residual analysis.
  • Evaluated predictive performance, hotspot identification, and uncertainty of estimated values.

Main Results:

  • The NB model incorporating the translog function demonstrated superior goodness-of-fit and residual analysis compared to the traditional NB model.
  • The translog function showed potential in improving predictive accuracy and hotspot identification in accident modeling.
  • The study confirmed the translog function's ability to handle non-constant elasticity, offering a more nuanced analysis.

Conclusions:

  • The translog function presents a promising, flexible alternative for accident modeling, outperforming traditional log-linear approaches.
  • This novel methodology enhances the reliable interpretation of explanatory variables' influence on accident frequency.
  • Further research is warranted to explore the full potential of the translog function in transportation safety.