Travel dynamics under MaaS bundles: The role of uncertainty in intertemporal choices and learning behavior

Mobility-as-a-Service (MaaS) bundles offer discounted access to multiple travel modes in exchange for a periodic subscription fee, encouraging travelers to better anticipate their mobility needs and thereby reducing operational uncertainty faced by service providers. However, this subscription model introduces complex intertemporal decision-making, as subscribers need to allocate a predetermined number of discounts across sequential trips within each bundle cycle. The uncertainty of real-world travel demand further complicates this allocation process. While these travel dynamics are crucial for designing effective MaaS bundles, little is known about how such intertemporal choices evolve over the long term. This study investigates travel dynamics under MaaS bundles across varying uncertainty levels. A dynamic discrete choice model (DDCM) that accounts for bundle constraints is developed to capture two dimensions of behavioral dynamics: (1) intertemporal decision-making within each subscription cycle, and (2) learning effects across multiple subscription cycles. As users gain experience with MaaS bundles, they place less weight on travel time and cost. Over repeated cycles, travelers demonstrate a declining preference for private cars and ride-sourcing, particularly for planned trips, whereas this adaptive shift is less pronounced under uncertain travel demand. Furthermore, users strategically conserve discounts when facing high demand uncertainty. These findings demonstrate a shift in user focus from trip-level attributes to bundle-level resource management, suggesting that the attractiveness of MaaS can be improved through tailoring bundle design and effective marketing strategy to accommodate travel demand uncertainty.