  Understanding the variability of people's travel patterns is key to transport
planning and policy-making. However, to what extent daily transit use displays
geographic and temporal variabilities, and what are the contributing factors
have not been fully addressed. Drawing on smart card data in Beijing, China,
this study seeks to address these deficits by adopting new indices to capture
the spatial and temporal variability of bus use during peak hours and
investigate their associations with relevant contextual features. Using
explainable machine learning, our findings reveal non-linear interaction
between spatial and temporal variability and trip frequency. Furthermore,
greater distance to the urban centres (>10 kilometres) is associated with
increased spatial variability of bus use, while greater separation of trip
origins and destinations from the subcentres reduces both spatial and temporal
variability. Higher availability of bus routes is linked to higher spatial
variability but lower temporal variability. Meanwhile, both lower and higher
road density is associated with higher spatial variability of bus use
especially in morning times. These findings indicate that different built
environment features moderate the flexibility of travel time and locations.
Implications are derived to inform more responsive and reliable operation and
planning of transit systems.
