Unleashing the synergies among rapidly evolving mobility technologies in a multi-stakeholder setting presents unique challenges and opportunities for addressing urban transportation problems. This paper introduces a novel synthetic participatory method that critically leverages 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 higher controllability and comprehensiveness on an SAEMS plan than that generated using a single LLM-enabled expert agent. Consequently, this 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 transportation systems.

翻译：在多利益相关方环境中释放快速发展的移动出行技术之间的协同效应，为解决城市交通问题带来了独特的挑战与机遇。本文提出了一种新颖的合成参与式方法，该方法关键性地利用大语言模型（LLMs）创建代表不同利益相关方的数字化身，以规划共享自动化电动出行系统（SAEMS）。这些可校准的智能体协同确定目标、构想并评估SAEMS替代方案，并在风险与约束条件下制定实施策略。蒙特利尔案例研究的结果表明，与使用单一LLM赋能专家智能体生成的方案相比，结构化且参数化的工作流程能为SAEMS规划提供具有更高可控性和全面性的输出。因此，该方法为经济高效地提升多目标交通规划的包容性与可解释性提供了一条前景广阔的途径，预示着我们在构想和规划可持续交通系统方面的范式转变。