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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Methods of Medium Optimization

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Related Experiment Video

Updated: Jun 4, 2026

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

Integrating generalized linear mixed models and XGBoost for safety performance function development on urban

Diana Al-Nabulsi1, Ali Alhawiti2, Norran Kakama Novat3

  • 1Department of Civil and Construction Engineering, Western Michigan University, 1903 W. Michigan Ave, Kalamazoo, MI, 49008, USA. diana.al-nabulsi@wmich.edu.

Scientific Reports
|June 2, 2026
PubMed
Summary

This study developed advanced safety models for urban roads in Michigan, finding Extreme Gradient Boosting (XGBoost) most accurately predicts crash frequency using traffic and road data.

Keywords:
Crash Frequency PredictionGeneralized Linear Mixed Models (GLMM)Safety Performance Functions (SPFs)Urban Arterial RoadsXGBoost

Related Experiment Videos

Last Updated: Jun 4, 2026

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

Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Machine Learning Applications

Background:

  • Urban arterial corridors face high crash risks due to traffic volume, complex designs, and varied road user interactions.
  • Accurate prediction of crash frequency is crucial for effective traffic safety management and investment prioritization.

Purpose of the Study:

  • To develop and evaluate Safety Performance Functions (SPFs) for urban arterial segments in Southeast Michigan.
  • To compare traditional statistical models with ensemble machine learning techniques for crash prediction.

Main Methods:

  • Employed Poisson, Negative Binomial, and Generalized Linear Mixed Models (GLMM).
  • Utilized ensemble machine learning: Random Forest and Extreme Gradient Boosting (XGBoost).
  • Assessed model performance using 5-fold cross-validation, focusing on predictive accuracy (R², RMSE).

Main Results:

  • XGBoost demonstrated superior predictive accuracy (R² = 0.835, RMSE = 28.65).
  • Key predictors identified across models include Annual Average Daily Traffic (AADT), segment length, speed limit, and pavement condition.
  • GLMM provided an interpretable, length-adjusted SPF formulation.

Conclusions:

  • Machine learning, particularly XGBoost, offers robust tools for predicting urban arterial crash frequency.
  • Findings support data-driven decision-making for identifying high-risk road segments and optimizing safety investments.
  • Models are adaptable for other regions with local calibration and data availability.