Abstract
In the last decade, the popularity of e-bikes has increased as they have shown potential to relieve congestion and aid the environment. However, with the increase of their popularity, there has been also an increase in their traffic crashes. This study aims to understand factors playing a role in the e-bike crash injury outcomes. The analysis uses 1,351 records of e-bike crashes to estimate random parameters multinomial logit models with heterogeneity in the means and variances of random parameters in four groups. This paper also seeks to provide insights into e-bike crash injury severities across gender (female versus male) and lighting conditions (daytime and nighttime) specific models. Numerous likelihood ratio tests were performed to justify splitting the data. It was found that a variety of factors relating to the weather and road characteristics, crash type, and rider’s demographics play a role in crash outcomes. Particularly interesting are findings relating to the rollover crashes increasing the likelihood of severe outcomes as well as gender specific effects with, for example, male riders have a higher probability of severe injuries during peak traffic hours. The findings can be used to support e-bike safety as well as advocate for a more nuanced and inclusive approach relating to e-bike travel.
Published Version
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