Exploring temporal variability in travel patterns on public transit using big smart card data

Abstract

Passengers generate travel behaviours on public transit, whose variations deserve an exploration with an aim to guide daily-updated managements. In this study, we investigate temporal variability in travel patterns for over 3.3 million passengers across 120 days who use public transit in Beijing. Temporal variability is characterized by a series of features in terms of space coverage, travel distance and travel frequency, based on which, passengers are clustered into two types, that is, commuters with daily travel routines, and non-commuters who do not. How, and to which extent, they change travel patterns over time are examined, with using approaches concerning multivariate regression and curve fitting. Results show that, (1) commuters are more likely to travel longer but cover less territory than non-commuters on weekdays, while the opposite patterns occur on weekends. The variation of day of week affects commuters less, compared to non-commuters, due to more fixed schedules, as expected; (2) travel distance and frequency are found to increase faster, more linearly, than space-coverage features, the last of which experience a progressive decreasing of marginal increases before reaching a plateau. The above findings facilitate transport practitioners to design sound management schemes for passengers in different categories.