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.

翻译：在多利益攸关方格局中释放快速演进的出行技术协同效应，为应对城市交通问题带来了独特挑战与机遇。本文提出一种新颖的合成式参与方法，通过关键性利用大语言模型（LLM）创建代表不同利益攸关方的数字孪生体，用于规划共享自动化电动出行系统（SAEMS）。这些可校准智能体通过协作识别目标、构想并评估SAEMS替代方案，以及在风险与约束条件下制定实施策略。蒙特利尔案例研究结果表明：相较于单一LLM驱动的专家智能体，结构化参数化工作流程生成的SAEMS方案具有更高的可控性与全面性。由此，该方法为成本效益化提升多目标交通规划的包容性与可解释性提供了可行路径，揭示了可持续公平交通系统构想与战略制定的范式转变。