Prioritizing harm reduction in instant delivery crashes: Predictors of pedestrian injury severity and targeted interventions.

Abstract

Instant delivery crashes (IDCs) pose a growing public health challenge. Standard crash databases rarely capture the fine-grained variables necessary to model severe pedestrian disability. To address this gap, we analyzed a sample of 732 adjudicated court verdicts involving pedestrian-IDC collisions. This specific data source captures a highly selected subset of litigated, severe events rather than the broader crash population. We applied a Hierarchical Generalized Ordered Probit (HGOP) model. This framework accommodates the ordinal nature of disability grades and unobserved age-related heterogeneity. To achieve model convergence, we consolidated fatalities and severe disabilities into a single highest-severity category. Our modeling reveals that older pedestrian age (65-74 years) and motorbike involvement strongly predict higher-grade disability. Conversely, female pedestrians and winter conditions correlate with lower injury severity. We also identified a negative monotonic association between rider liability and pedestrian injury grade. As legal rider responsibility increased, the predicted severity of pedestrian injuries systematically decreased. Lacking direct kinematic data, we hypothesize this liability association reflects distinct conflict typologies rather than direct physical causation. Translating these findings into effective harm reduction requires a two-tiered approach. Our statistical estimates directly support physical interventions, including age-proofed infrastructure and targeted motorbike regulations. Simultaneously, our descriptive behavioral data align with existing literature to advocate for platform governance reforms addressing algorithmic deadlines. Ultimately, these observational findings carry strict inferential boundaries. They apply exclusively to litigated crash populations and highlight the critical need for integrated clinical and kinetic data in future research.