Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data.

ABSTRACT: While the cost of road traffic fatalities in the U.S. surpasses $240 billion a year, the availability of high-resolution datasets allows meticulous investigation of the contributing factors to crash severity. In this paper, the dataset for Trucks Involved in Fatal Accidents in 2010 (TIFA 2010) is utilized to classify the truck-involved crash severity where there exist different issues including missing values, imbalanced classes, and high dimensionality. First, a decision tree-based algorithm, the Synthetic Minority Oversampling Technique (SMOTE), and the Random Forest (RF) feature importance approach are employed for missing value imputation, minority class oversampling, and dimensionality reduction, respectively. Afterward, a variety of classification algorithms, including RF, K-Nearest

SUBMITTER: Mohammadpour SI 

PROVIDER: S-EPMC10032500 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature