Modeling road accident severity with comparisons of logistic regression, decision tree and random forest

Mu Ming Chen*, Mu Chen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparametric data mining techniques, namely, logistic regression (LR), classification and regression tree (CART), and random forest (RF), to compare their prediction capability, identify the significant variables (identified by LR) and important variables (identified by CART or RF) that are strongly correlated with road accident severity, and distinguish the variables that have significant positive influence on prediction performance. In this study, three prediction performance evaluation measures, accuracy, sensitivity and specificity, are used to find the best integrated method which consists of the most effective prediction model and the input variables that have higher positive influence on accuracy, sensitivity and specificity.

Original languageEnglish
Article number270
JournalInformation (Switzerland)
Volume11
Issue number5
DOIs
StatePublished - 1 May 2020

Keywords

  • Decision tree
  • Logistic regression
  • Random forest
  • Road accident severity
  • transportation

Fingerprint Dive into the research topics of 'Modeling road accident severity with comparisons of logistic regression, decision tree and random forest'. Together they form a unique fingerprint.

Cite this