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Computer Science > Computational Engineering, Finance, and Science
Title: Large Language Models for Synthetic Participatory Planning of Synergistic Transportation Systems
(Submitted on 18 Apr 2024 (v1), last revised 23 Apr 2024 (this version, v3))
Abstract: 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.
Submission history
From: Jiangbo Yu [view email][v1] Thu, 18 Apr 2024 16:51:23 GMT (3133kb)
[v2] Sun, 21 Apr 2024 14:49:07 GMT (1255kb)
[v3] Tue, 23 Apr 2024 10:56:25 GMT (1255kb)
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