A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers

Abstract

Introduction: Motorcycle passengers comprise a considerable proportion of traffic crash victims. During a 5 year period (2006-2010) in Iran, an average of 3.4 pillion passengers are killed daily due to motorcycle crashes. This study investigated the main factors influencing crash severity of this group of road users.

Method: The Classification and Regression Trees (CART) method was employed to analyze the injury severity of pillion passengers in Iran over a 4 y ear period (2009-2012).

Results: The predictive accuracy of the model built with a total of 16 variables was 74%, which showed a considerable improvement compared to previous studies. The results indicate that area type, land use, and injured part of the body (head, neck, etc.) are the most influential factors affecting the fatality of motorcycle passengers. Results also show that helmet usage could reduce the fatality risk among motorcycle passengers by 28%.

Practical applications: The findings of this study might help develop more targeted countermeasures to reduce the death rate of motorcycle pillion passengers.

Keywords: Classification and regression trees; Crash severity; Motorcycle pillion passengers.