As emerging mobility modes continue to expand, many cities face declining bus ridership, increasing fiscal pressure to sustain underutilized routes, and growing inefficiencies in resource allocation. This study employs an agent-based modelling (ABM) approach calibrated through a large language model (LLM) using few-shot learning to examine how progressive bus route cutbacks affect passenger dissatisfaction across demographic groups and overall network resilience. Using IC-card data from Beijing's Huairou District, the LLM-calibrated ABM estimated passenger sensitivity parameters related to travel time, waiting, transfers, and crowding. Results show that the structural configuration of the bus network exerts a stronger influence on system stability than capacity or operational factors. The elimination of high-connectivity routes led to an exponential rise in total dissatisfaction, particularly among passengers with disabilities and older adults. The evolution of dissatisfaction exhibited three distinct phases - stable, transitional, and critical. Through the analysis of each stage, this study found that the continuous bus route reduction scenario exhibits three-stage thresholds. Once these thresholds are crossed, even a small reduction in routes may lead to a significant loss of passenger flow. Research highlights the nonlinear response of user sentiment to service reductions and underscore the importance of maintaining structural critical routes and providing stable services to vulnerable groups for equitable and resilient transport planning.

翻译：随着新兴出行方式的持续扩张，许多城市面临公交客流量下降、维持低利用率线路的财政压力加大以及资源配置效率持续走低等挑战。本研究采用通过大语言模型（LLM）结合少样本学习校准的智能体建模（ABM）方法，考察渐进式公交线路削减如何影响不同人群的乘客不满情绪及整体网络弹性。基于北京市怀柔区IC卡数据，LLM校准的ABM模型估算了与出行时间、等待时间、换乘次数和拥挤程度相关的乘客敏感参数。结果表明，公交网络的结构配置对系统稳定性的影响强于运力或运营因素。高连通性线路的取消导致总体不满情绪呈指数级上升，其中残疾人和老年乘客受影响尤为显著。不满情绪的演变呈现三个不同阶段：稳定期、过渡期和临界期。通过分阶段分析发现，连续削减公交线路场景呈现三阶段阈值效应。一旦突破这些阈值，即使仅减少少量线路也可能导致客流显著流失。该研究揭示了用户情绪对服务削减的非线性响应特征，强调在实现公平且富有弹性的交通规划中，维护结构性关键线路并为弱势群体提供稳定服务的重要性。