标题： 解决交通政策制定中的对齐问题：一种大语言模型方法 显示英文标题

标题： Addressing the alignment problem in transportation policy making: an LLM approach

摘要： 在交通规划中的一个关键挑战是，异质旅行者的集体偏好往往与模型驱动决策工具产生的政策相偏离。 这种不一致经常导致实施延迟或失败。 在此，我们研究大型语言模型（LLMs）是否能够帮助解决这一对齐问题，LLMs以其推理和模拟人类决策的能力而著称。 我们开发了一个多智能体仿真，在其中，LLMs作为代表城市中不同社区居民的智能体，参与一组交通政策提案的公投。 使用思维链推理，LLMs智能体提供排序选择或批准基础的偏好，这些偏好通过即时淘汰投票（IRV）进行聚合，以模拟民主共识。 我们使用GPT-4o和Claude-3.5实现了这个仿真框架，并将其应用于芝加哥和休斯顿。 我们的研究结果表明，LLM智能体能够近似合理的集体偏好并响应本地情境，同时表现出模型特定的行为偏差以及与基于优化的基准的适度偏离。 这些能力突显了LLMs在解决交通决策中的对齐问题方面的潜力和局限性。 显示英文摘要

摘要： A key challenge in transportation planning is that the collective preferences of heterogeneous travelers often diverge from the policies produced by model-driven decision tools. This misalignment frequently results in implementation delays or failures. Here, we investigate whether large language models (LLMs), noted for their capabilities in reasoning and simulating human decision-making, can help inform and address this alignment problem. We develop a multi-agent simulation in which LLMs, acting as agents representing residents from different communities in a city, participate in a referendum on a set of transit policy proposals. Using chain-of-thought reasoning, LLM agents provide ranked-choice or approval-based preferences, which are aggregated using instant-runoff voting (IRV) to model democratic consensus. We implement this simulation framework with both GPT-4o and Claude-3.5, and apply it for Chicago and Houston. Our findings suggest that LLM agents are capable of approximating plausible collective preferences and responding to local context, while also displaying model-specific behavioral biases and modest divergences from optimization-based benchmarks. These capabilities underscore both the promise and limitations of LLMs as tools for solving the alignment problem in transportation decision-making.