大规模城市仿真在社会科学、交通安全及交通政策研究中具有关键作用。近期研究表明，将大语言模型（LLM）作为智能体进行提示（prompting），可在城市尺度上生成类人的日常行为序列。然而，现有方法通常依赖少样本提示（few-shot prompting），导致智能体主要复现LLM自身的行为先验，而非目标人群的真实行为模式。本文提出CityReal——一种面向人本对齐的城市仿真模块化框架。CityReal将智能体建模为意图驱动的决策者，使其追求连贯的出行与活动计划，而非孤立的逐步选择；并依据经验与约束持续学习习惯与偏好，实现动态适应。为提升群体层面的真实性，我们为各行为模块学习文本适配器（textual adapters），使智能体决策与观测到的人群统计特征对齐。实验表明，CityReal在微观个体行为与宏观群体模式两个层面均显著提升了对真实人类行为的拟合度。该框架可扩展至数万个智能体规模，支持在不同城市情景下分析人群密度、场所热度、出行流以及居民福祉等指标，为城市仿真与预测提供可扩展的试验平台。

Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.