Temporal and out-of-sample prediction analysis of motorcyclists injury severities

Motorcycle crashes represent one of the most severe threats to road safety, with disproportionately high rates of fatalities and injuries compared to other road users. This study investigates the factors influencing motorcyclists' injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes, using the United Kingdom motorcycle crash data from 2016 to 2020. Motorcyclist injuries are categorized into minor, severe, and fatal. A random parameters multinomial logit model with a heterogeneity approach in means and variances is applied to model injury severities, addressing multiple layers of unobserved heterogeneities. To assess the temporal instability of significant factors, a series of likelihood ratio tests is conducted. The findings reveal transferability between SV and MV crashes, with significant temporal instability over the five years. The findings reveal substantial differences in determinants of SV and MV crashes: for example, motorcyclists aged 25-55 years involved in SV crashes had a 0.0292 higher probability of fatal injury, while elderly non-motorcycle drivers (over 65 years) significantly increased motorcyclists' likelihood of sustaining fatal injuries in MV crashes. The out-of-sample prediction simulation highlights substantial differences in injury severity probabilities across accident types (SV and MV) and over time. This research underscores the importance of considering SV and MV crash transferability and temporal instability to capture unobserved effects influencing motorcyclist injury severity. The statistically significant variances between SV and MV crash injury severity models offer insights for distinct policy interventions targeting SV and MV motorcycle rider safety.