The severity of freeway traffic accidents is collectively influenced by multiple factors, among which short-term traffic flow characteristics immediately preceding the incident play a particularly critical role. To systematically analyze the impact of short-term traffic flow states on injury severity, this study constructed a random parameter logit model accounting for mean heterogeneity, utilizing historical traffic accident data, ETC gantry transaction records, and meteorological data from Guangdong Province’s South 2nd Ring Expressway, Jiguang Expressway, and Western Coastal Expressway (2021—2022). The model was developed to investigate heterogeneous characteristics of accident contributing factors. A total of 29 potential variables were identified across four domains: road characteristics, environmental conditions, traffic flow features, and crash attributes. Three discrete model specifications were employed to model injury severity: a standard multinomial logit model, a random parameter logit model, and a random parameter logit model that accounts for mean heterogeneity. Comparative analysis of model goodness-of-fit using pseudo-R², akaike information criterion (AIC), and Bayesian information criterion (BIC) demonstrated that the random parameter logit model accounting for mean heterogeneity exhibits superior performance in goodness-of-fit. This specification more accurately captures the heterogeneous characteristics of accident contributing factors. Further analysis based on the average elasticity of variables reveals that, at the 99% confidence level, 22 parameters significantly affect injury severity. Specifically, features such as six-lane bidirectional roads and improved visibility significantly reduce injury severity, whereas longer road rescue handling time, higher average speed and proportion of large trucks, and greater speed differentials between large and small vehicles are associated with increased injury severity. The findings of this study offer valuable insights for improving freeway accident prevention and management strategies.