Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances

- Contribution factors on bicyclist-injury severities are studied.
- A random parameters logit model with heterogeneity in parameter means and variances is used.
- A wide range of variables that increase the likelihood of severe injuries in bicycle-motorized vehicle crashes are reported.
- Accounting for possible heterogeneity in means and variances of the random parameters improves overall model fit.

This paper investigates risk factors that significantly contribute to the injury severity of bicyclists in bicycle/motor-vehicle crashes while systematically accounting for unobserved heterogeneity within the crash data. Using the data from Los Angeles over a seven-year period (January 1, 2010 to December 31, 2016) a random parameters multinomial logit model of bicyclist-injury severity, with heterogeneity in parameter means and variances, is estimated to explore the effects of a wide range of variables on bicyclist injury-severity outcomes. Model estimation results show that many factors potentially affect the likelihood of severe injuries in bicycle/motor-vehicle crashes including bicyclist and driver race and gender, alcohol-impaired bicyclists or drivers, older bicyclists, riding or driving on the wrong side of road, drivers' unsafe speeding, bicyclist not wearing helmet, and so on. The findings of this research point toward the need to further study the contributing factors on the bicyclist injury severities.