人类移动生成旨在合成合理的轨迹数据，广泛应用于城市系统研究。尽管基于大语言模型的方法在生成日常轨迹方面表现优异，但在大规模社会事件期间的偏离性移动捕捉方面仍存在困难。这一局限源于两个关键缺口：（1）缺乏用于设计与评估的事件标注移动数据集；（2）现有框架在生成轨迹决策时，无法协调用户习惯模式与事件强制约束之间的冲突。本文通过双重贡献解决上述问题。首先，我们构建了首个涵盖三大事件（台风海贝思、新冠疫情、2021年东京奥运会）的事件标注移动数据集。其次，我们提出ELLMob，一种基于模糊表征理论的自对齐大语言模型框架，能够首先提取习惯模式与事件约束之间的竞争性理由，并通过迭代对齐生成既符合习惯又响应事件的轨迹。大量实验表明，ELLMob在所有事件中均优于现有最先进基线方法，验证了其有效性。代码与数据集已公开于 https://github.com/deepkashiwa20/ELLMob。

Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large-scale societal events. This limitation stems from two critical gaps: (1) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users' habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive. Extensive experiments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at https://github.com/deepkashiwa20/ELLMob.