# Synthetic Participatory Planning of Shard Automated Electric Mobility Systems

arXiv: https://arxiv.org/abs/2404.12317 · HTML: https://mlsanity.com/paper/2404.12317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence](https://mlsanity.com/paper/2309.11456/) (2023)
- [Toward Integrated Human-machine Intelligence for Civil Engineering: An Interdisciplinary Perspective](https://mlsanity.com/paper/2107.13498/) (2021)
- [Shared Autonomous Electric Vehicle Service Performance: Assessing the Impact of Charging Infrastructure and Battery Capacity](https://mlsanity.com/paper/1905.12160/) (2019)

## Built on by (most cited first, 3 of 3)

- [Agentic Vehicles for Human-Centered Mobility: Definition, Prospects, and Synergistic Co-Development with Vehicle Autonomy](https://mlsanity.com/paper/2507.04996/) (2025)
- [Modular Conversational Agents for Surveys and Interviews](https://mlsanity.com/paper/2412.17049/) (2024)
- [AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?](https://mlsanity.com/paper/2606.13696/) (2026)

## Abstract (arXiv)

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.