Crash injury severity analysis of E-Bike Riders: A random parameters generalized ordered probit model with heterogeneity in means

Electric bike (E-bike) fatal crashes have increased by 34% during the period between 2014 and 2016, raising a great challenge for traffic safety in China. This study examines effects of road characteristics, environmental factors, crash characteristics rider demographic factors on e-bike riders' injury severity. A total 2222 police-reported records riders representative developing area China-Hunan province from to 2016 is used current study. To account ordinal nature severity incorporate unobserved heterogeneity at observation level, random parameters generalized ordered probit model with means (RGOP-HM) applied analysis. For examining efficiency proposed modeling severity, models, models were also estimated. The superiority RGOP-HM terms fitness statistics indicates importance relaxing limitations traditional probability methods. results revealed wide range associated injuries, including horizontal curves, roads high posted speed limit, sign-controlled intersections, dim light, unlighted darkness, single-vehicle crashes, collisions heavy motorized vehicle, age over 44 (45–59, above 59), rural areas. Based contributing several implications are perspective engineering, education, enforcement (3E). study's findings could provide references development targeted countermeasures improve