Unveiling the influence of behavioural, built environment and socio-economic features on the spatial and temporal variability of bus use using explainable machine learning

TL;DR

This study uses explainable machine learning on smart card data from Beijing to analyze how behavioural, built environment, and socio-economic factors influence the spatial and temporal variability of bus use, providing insights for transit planning.

Contribution

It introduces new indices to measure variability and applies explainable machine learning to uncover complex interactions between features and bus use patterns.

Findings

Distance to urban centers increases spatial variability.

Route availability affects both spatial and temporal variability.

Road density has a non-linear impact on bus use variability.

Abstract

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.