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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

76
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
76
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

94
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
94
Profile Leveling and Cross Sections01:26

Profile Leveling and Cross Sections

443
Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the...
443
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.7K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.7K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

479
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:
479

You might also read

Related Articles

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

Sort by
Same author

How do macroscopic traffic flow parameters affect time spent in conflict on freeways? A comprehensive analysis using hazard-based duration models.

Traffic injury prevention·2025
Same author

Investigating the contributors to hit-and-run crashes using gradient boosting decision trees.

PloS one·2025
Same author

Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data.

Sensors (Basel, Switzerland)·2024
Same author

Study on ring-road incident duration based on latent class accelerated hazard model.

PloS one·2024
Same author

Treatment patterns and prognosis of patients with clear cell adenocarcinoma of the cervix: a population-based cohort study.

International journal of surgery (London, England)·2024
Same author

Lactobacillus inoculation mediated carboxylates and alcohols production from waste activated sludge fermentation system: Insight into process outcomes and metabolic network.

Bioresource technology·2024

Related Experiment Video

Updated: Aug 20, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.9K

Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model.

Lan Wu1, Qi Shen1, Gen Li1

  • 1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.

International Journal of Environmental Research and Public Health
|November 26, 2022
PubMed
Summary

Vehicle speed and driver actions significantly influence automobile driver injury severity at highway-rail grade crossings. Truck drivers

Keywords:
grade crossingsinjury severitymixed logit modelvehicle type

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
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

3.4K

Related Experiment Videos

Last Updated: Aug 20, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
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

3.4K

Area of Science:

  • Transportation Safety
  • Traffic Engineering
  • Accident Analysis

Background:

  • Highway-rail grade crossings (HRGCs) present unique risks for vehicle drivers.
  • Understanding factors influencing injury severity at these crossings is crucial for safety improvements.

Purpose of the Study:

  • To identify and compare factors affecting injury outcomes for automobile and heavy vehicle drivers at HRGCs.
  • To inform differentiated safety policies for different vehicle types.

Main Methods:

  • Utilized a mixed logit model.
  • Analyzed the Federal Railroad Administration (FRA) dataset (2011-2020, n=194,385).

Main Results:

  • Injury severities differ significantly between automobile and truck/truck-trailer drivers at HRGCs.
  • Vehicle and train speeds impact injury severity for both automobile and truck drivers.
  • Driver characteristics (gender, actions) are key for automobile drivers, while HRGC attributes (space, rurality, warning devices) are significant for truck drivers.

Conclusions:

  • Differentiated factors influence injury severity based on vehicle type at HRGCs.
  • Findings support the development of tailored safety interventions for automobile and truck drivers.