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

  • CCDI Group Co., Ltd.
  • Suzhou Industrial Park Monash Research Institute of Science and Technology
  • Monash University
  • The University of Hong Kong

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number104213
JournalJournal of Transport Geography
Volume125
Issue number104214
DOIs
Publication statusPublished - 1 May 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 5 - Gender Equality
    SDG 5 Gender Equality
  3. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  4. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Driver
  • Efficiency
  • Gender
  • Holiday
  • Ride-hailing
  • Safety
  • Artificial intelligence (AI)
  • Geographically weighted random forest (GWRF)

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