Motorcycles represent an increasing proportion of traffic fatalities in the United States, accounting for more than 12.7% of the total traffic casualties within 2005–2014. Specifically, in North Carolina, motorcycles comprise less than 1% of vehicles involved in crashes but account for more than 7% of total fatalities, representing a top state in the United States.
This study tries to investigate the motorcycle crashes in North Carolina more in depth. In doing so, five years’ (2009–2013) worth of crash data was obtained from the Federal Highway Administration’s Highway Safety Information System database. A partial proportional odds (PPO) logistic regression model was developed to examine the influence of the explanatory variable on the ordered dependent variable, that is, injury severity. Moreover, two other popular ordered-response models, that is, proportional odds and non-proportional odds models, as well as one similar unordered-response model, that is, multinomial logit model, were also developed to evaluate their performances compared to the PPO model. Older riders, DUI riding, not wearing helmets, crashes during summer and weekends, darkness, crashes with fixed objects, reckless riding, and speeding were found to increase the severity of injuries. In contrast, younger riders, winter season, adverse weather condition, and wet surface were associated with lower injury severities. Furthermore, crashes in rural areas, overturn/rollover, and crashes happened while negotiating a curve showed fluctuating effects of injury severity. According to two information criteria calculated for all three developed models fitted to the same data, the PPO model was found to outperform the other models and provide more reliable results.
Based on the obtained average direct pseudo-elasticities, this study determines the effect of the various identified variables and develops several safety countermeasures as a resource for policy-makers to prevent or mitigate the severity of motorcycle crashes in North Carolina.
Pour-Rouholamin, M., Jalayer, M., & Zhou, H. (2017). Modelling single-vehicle, single-rider motorcycle crash injury severity: An ordinal logistic regression approach. International Journal of Urban Sciences, 21(3), 344–363. https://doi.org/10.1080/12265934.2017.1311801