Understanding Multi-Source Impacts on Airport Ride-Hailing Vehicle Queueing: Insights from a Time-Segmented Framework

Ride-hailing drivers often face queueing challenges at airports owing to mismatches between passenger demand and vehicle supply, which can affect urban mobility efficiency. Queue length and waiting time are key performance metrics reflecting the interaction between airport passengers and ride-hailing services within the broader urban transport system. This study explores the impacts of multi-source exogenous factors on the queueing performance of airport ride-hailing vehicles and develops a generalizable analytical framework to support adaptive operation. The proposed optimal segmentation for circular samples (OSCS) algorithm enables time segmentation based on queueing dynamics while bypassing traditional calendar-based divisions. This segmentation allows precise regression and temporally interpretable analysis. Within each time segment, generalized additive models (GAMs) capture relationships between influencing factors and queueing metrics. Using Hangzhou Airport in China as a case study, we apply the OSCS-GAM framework, which divides the daily operational timeline into three statistically distinct segments, morning, afternoon, and night, revealing differentiated queueing characteristics. Regression results reveal that air passenger volume predominantly drives vehicle queueing dynamics, while other factor categories, including weather, urban traffic conditions, and weekday-weekend patterns, independently exhibit temporally heterogeneous impacts across the three daily segments. Our approach captures the temporal and contextual dependencies of airport ride-hailing vehicle queues, offering insights into optimizing resource utilization within urban transport systems. These findings inform municipal and airport authorities, as well as ride-hailing companies, supporting the development of adaptive operational strategies that promote efficient and sustainable urban transportation.