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.

通过批判性利用大型语言模型（LLM）创建数字化代表不同利益相关者来规划共享自动化电动出行系统（SAEMS）的合成参与方法，本文介绍了一个在快速演变的移动技术多利益相关者环境中释放协同效应以解决城市交通问题的新方法。通过蒙特利尔案例研究的结果表明，这种结构化和可调参数的工作流程相较于单个 LLM 启用的专家代理生成的 SAEMS 计划，提供了具有较高可控性和综合性的输出。因此，该方法为改善多目标交通规划的包容性和可解释性提供了一条具有成本效益的途径，暗示了我们对可持续和公平交通系统的设想和战略的范式转变。