Unleashing the synergies of rapidly evolving mobility technologies in a multi-stakeholder landscape presents unique challenges and opportunities for addressing urban transportation problems. This paper introduces a novel synthetic participatory method, critically leveraging large language models (LLMs) to create digital avatars representing diverse stakeholders to plan shared automated electric mobility systems (SAEMS). These calibratable agents collaboratively identify objectives, envision and evaluate SAEMS alternatives, and strategize implementation under risks and constraints. The results of a Montreal case study indicate that a structured and parameterized workflow provides outputs with high controllability and comprehensiveness on an SAEMS plan than generated using a single LLM-enabled expert agent. Consequently, the approach provides a promising avenue for cost-efficiently improving the inclusivity and interpretability of multi-objective transportation planning, suggesting a paradigm shift in how we envision and strategize for sustainable and equitable transportation systems.

中文翻译：

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用于共享自动电动交通系统综合参与规划的大型语言模型

在多利益相关者的环境中释放快速发展的移动技术的协同效应，为解决城市交通问题带来了独特的挑战和机遇。本文介绍了一种新颖的综合参与方法，批判性地利用大语言模型（LLM）来创建代表不同利益相关者的数字化身，以规划共享自动电动交通系统（SAEMS）。这些可校准代理协作确定目标、设想和评估 SAEMS 替代方案，并在风险和约束下制定实施策略。蒙特利尔案例研究的结果表明，与使用单个支持 LLM 的专家代理生成的结果相比，结构化和参数化的工作流程在 SAEMS 计划上提供的输出具有更高的可控性和全面性。因此，该方法为经济高效地提高多目标交通规划的包容性和可解释性提供了一条有前途的途径，表明我们如何设想和制定可持续和公平的交通系统战略的范式转变。