An Analysis of The Impact Factors of Head Injuries of Two-wheeler Riders Using a Latent Class Logit Model

This paper studies the impact factors of head injuries of two-wheeler riders via a novel latent class Logit model. The seriousness of the head injuries of the riders is used as the dependent variable, while the factors of drivers, vehicles, roads, environment, and characteristics of collisions are taken as independent variables. A multinomial Logit model is developed with a significance level of 0.05. On this basis, the optimal number of classes is determined according to the goodness of fit. A latent class Logit model is developed based on 2806 two-wheeler collision data collected by the China In-depth Accident Study (CIDAS). According to the results, the model divides accident samples into two distinct categories. The two groups differ significantly in terms of parameter values, variable distribution characteristics, and the likelihood of predicting the outcome. Specifically, accidents with characteristics such as"two-wheelers initial speed is greater than 30 km/h"and"throwing distance is greater than 10 meters"are more likely to be classified as Class 1, which refers to the riders with more severe head injuries. In addition, severer head injuries are likely to occur under the following scenarios: including when a rider is over fifty, the colliding vehicle is a commercial truck, the two-wheeler is a motorcycle, the accident occurs outside a city, the two-wheeler is traveling above 30 km/h, the head collides with the glass, and the distance to the collision site after the collision is greater than 5 meters. Moreover, the risk of serious two-wheeler collisions is higher when a car driver intends to park or change lanes. Helmets are shown to reduce head injuries among riders.