Safety Analysis of Motorcycle Crashes in Seoul Metropolitan Area, South Korea: An Application of Nonlinear Optimal Scaling Methods

Abstract

This study identifies the critical factors that affect motorcycle crash severity based on Korean motorcycle crash data in 2009. Motorcyclists, the environment, roadways, other vehicles involved in the crashes, and traffic flow characteristics were used as variables for identifying critical factors. Multivariable statistical methods were used to analyze the data, including categorical principal components analysis (CatPCA) and nonlinear canonical correlation analysis (NLCCA). The results indicate that the following factors are the most critical in increasing motorcycle crash severity: age (motorcyclists in their teens and over fifty years old), motorcycle speed over 30 km/h, speed over 50 km/h for other vehicles involved in the crash, crashes with heavy vehicles such as buses and trucks, crashes on roadways less than six meters wide, crashes at curved sections, crashes at basic roadway segments without any speed control facilities, and head-on crashes. These findings are expected to serve as a valuable reference for formulating remedial policy measures to decrease the severity of motorcycle crashes on roadways in the Seoul metropolitan area of South Korea.