Motorcycle crash injury severity in Brazil: an analysis using mixed ordered response models

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Flávio Cunto (Author) Other; Sara Ferreira (Author) FEUP

Conference proceedings International

Title: TRB 94th Annual Meeting

Pages: 1-13

Transportation Research Board

Washington DC, USA

Scientific classification

FOS: Engineering and technology > Civil engineering

CORDIS: Technological sciences > Engineering > Civil engineering > Infrastructures engineering

Other information

Abstract (EN): Brazilian traffic environment has experienced a disproportionate growth in motorcycle use over the last 15 years. Unfortunately the same trend has been observed for crash frequency and severity in the category in part by their relative exposure as well as vulnerability. This study investigates factors that influence the severity of motorcycle accidents in urban streets of Fortaleza. Traditional and mixed ordered logit models were calibrated using a sample of 3,232 observations of traffic accidents from 2004 to 2011. Physical levels of injury inflicted to motorcyclists were grouped as "no apparent injury" (17.3%), "slight injury" (68.2%), "serious injury" (13.1%) and "fatal injury" (1.4%). The models were developed using variables related to helmet usage, time of the day, day of the week, road surface conditions at the time of the crash, type of motorcycle usage, age and gender of drivers. Results suggested that motorcyclists using helmets reduced by 9% their chances of suffering severe and fatal injuries after the crash. Accidents during the daylight as well as on weekdays presented lower risk of resulting in fatal injuries than during night time and weekends and crashes involving motorcyclists above 61 years old have 22% more probability of resulting in severe and fatal injuries as compared to young riders (18 to 40 years old). Most of these findings can be associated with commonly reported risky behavior from motorcyclists which includes speeding, DUI, improper lane changes, red light running and unsafe longitudinal and transversal headway.

Language: English

Type (Professor's evaluation): Scientific