Abstract
Amidst a global decline in bus ridership, this study pioneers a longitudinal approach to understanding individual-level transitions and churning in urban bus systems. Utilizing a novel framework that leverages smart card data, we construct and analyze user behavior transition matrices over time, employing Markov processes and the Chapman-Kolmogorov Equation. Our analysis, derived from a 22-month dataset from Shenzhen, reveals a two-stage churning process: users first decrease travel frequency before transitioning to irregular travel patterns. Crucially, this study introduces targeted retention policies, including tiered usage incentives and personalized communication strategies, aimed at different stages of the user lifecycle. By offering free subsequent trips to irregular travelers and combining policy approaches for users at high risk of churning, we provide actionable insights for transit operators to counter the trend of declining ridership.
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