Kavli Affiliate: Ke Wang
| First 5 Authors: , , , ,
| Summary:
While ride-hailing services offer increased travel flexibility and
convenience, persistent nighttime safety concerns significantly reduce women’s
willingness to use them. Existing research often treats women as a homogeneous
group, neglecting the heterogeneity in their decision-making processes. To
address this gap, this study develops the Latent Class Integrated Choice and
Latent Variable (LC-ICLV) model with a mixed Logit kernel, combined with an
ordered Probit model for attitudinal indicators, to capture unobserved
heterogeneity in women’s nighttime ride-hailing decisions. Based on panel data
from 543 respondents across 29 provinces in China, the analysis identifies two
distinct female subgroups. The first, labeled the "Attribute-Sensitive Group",
consists mainly of young women and students from first- and second-tier cities.
Their choices are primarily influenced by observable service attributes such as
price and waiting time, but they exhibit reduced usage intention when matched
with female drivers, possibly reflecting deeper safety heuristics. The second,
the "Perception-Sensitive Group", includes older working women and residents of
less urbanized areas. Their decisions are shaped by perceived risk and safety
concerns; notably, high-frequency use or essential nighttime commuting needs
may reinforce rather than alleviate avoidance behaviors. The findings
underscore the need for differentiated strategies: platforms should tailor
safety features and user interfaces by subgroup, policymakers must develop
targeted interventions, and female users can benefit from more personalized
risk mitigation strategies. This study offers empirical evidence to advance
gender-responsive mobility policy and improve the inclusivity of ride-hailing
services in urban nighttime contexts.
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