Spatial-temporal insights into gender gaps in East Asian ride-hailing: Workload, efficiency, nighttime safety, and operational patterns

This study investigates gender differences in the operational strategies of ride-hailing drivers in Suzhou, China, using identity-confirmed trip data from October 2023. Four novel operational metrics (workload, earning capacity, operational efficiency, and willingness to work far from home) were developed to quantify gender-based operational preferences between male drivers and female drivers, who were further divided into age groups. The Geographically Weighted Random Forest (GWRF) model was applied to examine spatial correlations with socioeconomic and built environment factors in order to understand the spatial and temporal preferences of the drivers. Statistics reveal only 5 % of the drivers are female and within that fewer (5 %) are young female drivers. Spatial-temporal analysis indicated that female drivers generally work shorter distances, earn less, and prioritize trips closer to home, which may be influenced by family caregiving responsibilities, but exhibit similar operational efficiency (indicated by revenue per hour) as male drivers under comparable demand conditions. Age gaps are further observed among female drivers: senior female drivers worked significantly longer during holidays but avoided nighttime operations similarly to younger females, reflecting the former group's heavier financial burdens related to family responsibilities and shared safety concerns with the latter. The results of GWRF show little impact of street safety on daytime pick-up locations across genders which is different than Western contexts. However, nighttime spatial preferences significantly differ among younger female drivers compared to older and male drivers, reflecting higher safety concerns. These insights inform platform operators to prioritize order allocation near female drivers' residences and improve safety measures, thereby supporting gender equity and inclusivity at a government level. • Proposed metrics to quantify workload, revenue, location preference, and efficiency differences. • Female drivers prefer shorter trips closer to home, younger females more so. • Holidays boost workload and earnings for both male and senior female drivers. • GWRF model reveals spatial correlations with built environment factors. • Nighttime orders show stronger links to safety and industrial landuse, especially for young women.