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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

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

Related Experiment Video

Updated: May 10, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes.

Rongjie Yu1, Mohamed Abdel-Aty

  • 1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States. rongjie.yu@knights.ucf.edu

Accident; Analysis and Prevention
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

This study analyzed freeway crashes, developing models to identify factors contributing to single-vehicle (SV) and multi-vehicle (MV) accidents. Findings reveal key variables impacting crash risk and offer insights for improving mountainous freeway safety.

Keywords:
Bayesian logistic regressionBivariate Poisson-lognormal modelMountainous freewayRandom parameterSafety performance functions

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Related Experiment Videos

Last Updated: May 10, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Statistical Modeling

Background:

  • Mountainous freeways present unique safety challenges due to complex geometry and environmental factors.
  • Understanding the distinct contributing factors for single-vehicle (SV) and multi-vehicle (MV) crashes is crucial for targeted safety interventions.

Purpose of the Study:

  • To develop and compare aggregate and disaggregate statistical models for analyzing SV and MV crashes on a mountainous freeway.
  • To identify significant explanatory and exposure variables influencing crash occurrence.
  • To evaluate real-time crash risk using microscopic models that account for temporal and spatial variations.

Main Methods:

  • Aggregate analysis using Bayesian bivariate Poisson-lognormal and Bayesian hierarchical Poisson models with 5 years of crash data.
  • Disaggregate analysis employing multi-level Bayesian logistic regression with 1 year of real-time traffic and weather data.
  • Incorporation of geometric characteristics, traffic volume, segment length, seasonal variations, and unobserved heterogeneity.

Main Results:

  • The Bayesian bivariate Poisson-lognormal model demonstrated superior performance over the Bayesian hierarchical Poisson model.
  • Significant explanatory and exposure variables differed between SV and MV crashes.
  • Real-time models indicated that crash occurrence effects vary by season and crash unit, with geometric characteristics contributing to segment variations.

Conclusions:

  • Statistical models effectively identify contributing factors for different crash types on mountainous freeways.
  • Accounting for unobserved heterogeneity and real-time data enhances crash prediction and classification abilities.
  • The findings provide a foundation for developing data-driven safety improvements for challenging freeway environments.