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Synthetic Participatory Planning of Shared Automated Electric Mobility Systems

Author

Listed:
  • Jiangbo Yu

    (Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada)

  • Graeme McKinley

    (Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada)

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.

Suggested Citation

  • Jiangbo Yu & Graeme McKinley, 2024. "Synthetic Participatory Planning of Shared Automated Electric Mobility Systems," Sustainability, MDPI, vol. 16(13), pages 1-32, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5618-:d:1426368
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    References listed on IDEAS

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    3. Florian Dandl & Michael Hyland & Klaus Bogenberger & Hani S. Mahmassani, 2019. "Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets," Transportation, Springer, vol. 46(6), pages 1975-1996, December.
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    Cited by:

    1. Yu, Jiangbo & Hyland, Michael F., 2025. "Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    2. Yu, Jiangbo, 2025. "Preparing for an agentic era of human-machine transportation systems: Opportunities, challenges, and policy recommendations," Transport Policy, Elsevier, vol. 171(C), pages 78-97.

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