Impact of Accident Characteristics on Rider Injuries: Analysis of Motor Vehicle vs. Electric Two-Wheeler Collision Data

Analyses of electric two-wheeler (E2W) accident causes often overlook individual heterogeneity and non-linear effects among influencing factors, causing risk prediction models to fail. Based on 742 motor vehicle–E2W collisions from the China In-Depth Accident Study (CIDAS) database, we established the correlated random parameter ordered probit model with heterogeneity in means (CRPOPH) and the generalized additive model (GAM) to analyze the effects of discrete and continuous variables on E2W accident severity. The results show that injury severity increases significantly for the following characteristics: male, 51–60 years old, <170 cm, SUV or truck, intersection road, non-asphalt pavement, daytime and side impact accidents. With all other factors held constant, injury severity decreases with increasing seat height and seat–handlebar distance; when the wheelbase is approximately between 1000 mm and 1400 mm, injury severity tends to be the lowest; when the E2W speed is about 15–30 km/h, injury severity is positively correlated with speed; in other speed ranges, E2W speed is negatively correlated with injury severity. Compared with traditional linear models, the proposed method achieves both high interpretability and high fitting accuracy in modeling E2W accident severity.