The Effect of Age on Motorcyclist Injury Severities in Multi-Vehicle Motorcycle Crashes: Accounting for Unobserved Heterogeneity and Insights From Out-of-Sample Prediction

Abstract

This paper studies the factors affecting injury severities involving motorcyclists of different age groups (under 25, 25-55, 55 and above) based on the random parameter logit model. Data collected from motorcycle crashes in the UK between 2017 and 2020 are utilized. The motorcyclist injury severity outcomes are categorized as follows: minor injury, severe injury, and fatal injury. The results of the likelihood ratio tests showed that transferability in multi-vehicle motorcycle crash injury severity involving motorcyclists of different age groups. The results of the modeling revealed significant variations in the factors that impact motorcycle crashes among three different age groups, including rider characteristics (such as male riders), motorcycle and non-motorcycle characteristics (such as vehicle running status preceding collision, age of the vehicle, and non-motorcycle vehicle type), roadway and environmental conditions (such as weather condition, speed limit, and road type), temporal-related characteristics (such as day of the week), and crash-related characteristics (such as crash types). The models demonstrate the existence of unobserved heterogeneity for three statistically significant variables, including the truck-involved, rear-end collision, and morning off-peak hours indicator in the under 25 age group crash model, and vehicle straight movement in the 25-55 age group crash model, and passenger car and sideswipe in the 55 and above age group crash model. Further, there are substantial differences in injury severity probabilities involving motorcyclists of different age groups by comparing prediction results based on out-of-sample prediction simulation. This paper emphasized the importance of revealing different age groups' crash transferability and heterogeneity. The statistically significant differences involving age group crash injury severity models highlight the importance of age-targeted policies for motorcycle safety.INDEX TERMS Multi-vehicle motorcycle crashes, age groups, injury severity, random parameters logit model, out-of-sample prediction.