Understanding user responses to incentives in shared micromobility with LLM-based digital twins

Shared micromobility has emerged as a crucial component of urban transportation, yet high operational costs remain a significant bottleneck. User incentives, primarily offered as fare discounts, are widely adopted to mitigate these issues; however, their effectiveness relies heavily on a thorough understanding of user choice behaviors. This study investigates user responses to incentive mechanisms in shared micromobility using a novel large language model (LLM)-based behavioral simulation framework. Profile-based user digital twins are developed to emulate responses under incentive scenarios at scale. Two types of incentives are analyzed: station-based incentives (spatial relocation) and vehicle-based incentives (battery adjustment). The fidelity of these digital twins is evaluated across dimensions of stability (consistency testing), rationality (rationale analysis), and validity (empirical benchmarking), suggesting their ability to capture key stated preference patterns under controlled scenarios. Findings highlight user heterogeneity, with younger, lower-income, and non-commuting individuals showing greater acceptance than older, higher-income, and commuting users. Past interactions influence incentive effectiveness, as prior acceptance promotes engagement and negative experiences deter it, with both effects initially strengthening before diminishing. However, exposure frequency has no significant impact on incentive acceptance. Vehicle-based incentives can serve as an efficient baseline mechanism for routine operations with lower adoption barriers. In contrast, station-based incentives encounter greater initial resistance, but are more responsive to financial rewards and suited for targeted rebalancing. The work offers practical insights for incentive design in shared micromobility and demonstrates the potential of LLMs in behavioral simulation for transportation.