The rapid expansion of the gig economy has increased instant delivery crashes (IDCs), yet the determinants of rider disability remain unclear. This study analyzed 633 adjudicated rider injury cases (2015–2023) from Chinese legal judgments to identify risk factors for the Highest Assessed Disability Grade (HADG). We integrated sociodemographic, vehicular, spatiotemporal, and crash-mechanism variables into ordered logit models to quantify risk determinants. Results indicate that while most adjudicated injuries were non-permanent, lower-limb fractures constituted the primary mechanism of established disability. Significant risk factors for severe disability included rider age over 50, migrant status, and collisions involving automobiles. Notably, a counter-intuitive “liability paradox” emerged: riders bearing secondary liability faced the highest risk of severe disability (OR = 1.943), surpassing those with primary liability (OR = 1.732) due to kinetic asymmetry. Addressing these distinct mechanisms requires a multi-layered framework. We recommend accelerating physical infrastructure segregation to decouple riders from high-energy motor traffic. Conversely, to curb the behavioral precursors of rider-at-fault crashes, we propose a “traffic safety credit system” combining dispatch suspensions with mandatory re-education. This study validates the utility of forensic data, offering actionable, evidence-based pathways to mitigate the severe human costs of the on-demand sector.
Yan et al. (Mon,) studied this question.