Synthetic Participatory Planning of Shared Automated Electric Mobility Systems

Abstract

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.