  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.
