Impact of Dyadic Age Discrepancy on Non-Delivery e-Bike Rider Injury Severity in Collisions with Food Delivery Riders

The paper presents a Bayesian Hierarchical Generalized Ordered Probit model quantifying how the dyadic age difference between delivery riders (DR) and Non-Delivery e-bike Riders (NDR) determines traffic collision injury severity. Analysis of 741 adjudicated civil verdicts from China’s OpenLaw platform demonstrated a strict negative association between this relational age gap and physical trauma outcomes. The dyadic age difference yielded a posterior coefficient of −0.13. Shifting the DR from the 5th to the 95th relative age percentile increased the NDR “Not disabled” probability by 22.25 percentage points. This identical interval concurrently decreased severe Grade 8+ injury probabilities by 10.29 percentage points. Conversely, NDR absolute age positively escalated injury severity across the clinical spectrum. These statistical estimates suggest that older DR are associated with reduced physiological vulnerability in aging commuter populations. Current automated dispatch algorithms optimize exclusively for delivery speed and completely atomize the gig workforce. Platform architects can reconfigure these digital systems to proactively pair experienced older riders with younger peers during overlapping dispatch windows. This algorithmic peer-mentoring strategy bypasses ineffective asynchronous training modules. Re-engineering dispatch logic directly operationalizes the relational age gradient to mitigate the systemic trauma burden threatening modern mobility.