The Study on Optimization of Rider Resource Allocation and Performance Evaluation Based on Data-Driven Approach

This study explores the key factors affecting delivery rider performance, with a focus on the impact of different contexts such as holidays, adverse weather, and peak periods on rider performance. By analysis riders' personal characteristics (such as age and work experience) and external environmental factors (such as weather), the study identifies performance differences among different rider groups in various contexts. The research finds that riders with 1-2 years of registration tenure exhibit the highest daily work rates, while riders from Heilongjiang perform better in all contexts compared to those from other provinces. Additionally, the study examines the impact of rider fatigue on timely delivery rates, revealing a significant correlation between continuous work hours, rest days, and rider performance. This research provides specific recommendations for optimizing rider resource allocation and improving service quality.