Ali Tavakoli Kashani1, Rahim Rabieyan2, Mohammad Mehdi Besharati3. 1. School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. Electronic address: alitavakoli@iust.ac.ir. 2. School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. Electronic address: rahimrabie@civileng.iust.ac.ir. 3. School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. Electronic address: mehdi_besharati@civileng.iust.ac.ir.
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.
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.
Authors: Lluís Sanmiquel; Marc Bascompta; Josep M Rossell; Hernán Francisco Anticoi; Eduard Guash Journal: Int J Environ Res Public Health Date: 2018-03-07 Impact factor: 3.390
Authors: Li Zhou; Chun Guo; Yunxiao Cui; Jianjun Wu; Ying Lv; Zhiping Du Journal: Int J Environ Res Public Health Date: 2020-04-17 Impact factor: 3.390