Determinants of delivery rider disability severity based on judicial adjudication records in China

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-04-13
DOI
https://doi.org/10.1038/s41598-026-43663-w
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determinants of delivery rider disability severity based on judicial adjudication records in China

Mingyang Du, Jun He, Xiaofei Ye, Tao Wang et al.
Scientific Reports
Traffic and Road Safety
article

Determinants of delivery rider disability severity based on judicial adjudication records in China

Mingyang Du, Jun He, Xiaofei Ye, Tao Wang, Jun Chen, Tong Ye, Xingchen Yan
article en

Abstract

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.

Scientific Reports
Ningbo University (CN), Nanjing Forestry University (CN), Guilin University of Electronic Technology (CN), Southeast University (CN)
National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Natural Science Foundation of Ningbo, Natural Science Research of Jiangsu Higher Education Institutions of China, Humanities and Social Science Fund of Ministry of Education of China, Natural Science Foundation of Zhejiang Province
Gender equality
Openalex Percentile: Top 17%
Traffic and Road Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.