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.
Abstract (translated)
在多利益相关者的背景下,释放快速发展的移动技术之间的协同作用面临着独特的挑战和解决城市交通问题的机遇。本文介绍了一种新颖的合成参与方法,通过大型语言模型(LLMs)创建数字代表不同利益相关者的虚拟代理,规划共享自动电动交通系统(SAEMS)。这些可调节的代理合作确定目标、展望和评估SAEMS备选方案,并制定策略实施风险和约束条件。蒙特利尔案例研究的结果表明,使用结构化和参数化的工作流程可以比使用单一LLM启用的专家代理规划SAEMS计划产生具有高可控制性和全面性的输出。因此,该方法为改善多目标交通规划的包容性和可解释性提供了有前途的途径,表明了我们对于可持续和公平交通系统的愿景和策略发生了范式转移。
URL
https://arxiv.org/abs/2404.12317
