An urban mobility system serves as a highly intricate and nonlinear mega-system facilitating the movement of people, goods, and services across spatio-temporaldomains. This complexity stems from factors such as intricate interactions between transportation supply and demand, and the inherent stochastic nature of an open, heterogeneous, and adaptable system. Successfully comprehending and navigating this system presents a challenge. Yet, a remarkable opportunity emerges with the growing availability of multi-source data in urban mobility and various sectors, combined with the recent advancements in large-scale machine learning (ML) models. In this paper, we introduce a novel conceptual framework, the HUGE (Hierarchically Unified GEnerative) foundation model, to address multifaceted computational tasks and decision-making problems embedded in urban mobility systems. We delve into the core technologies and their seamless integration to realize this framework, highlighting its potential to harness substantial data analytics, hierarchical ML methodologies, and domain-specific knowledge. The conceived framework has the potential to revolutionize urban mobility system planning, design, construction, and management in a digital and intelligent manner.

中文翻译：

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城市移动基础模型：文献综述和分层视角


城市交通系统是一个高度复杂和非线性的巨型系统，促进了人员、商品和服务在时空领域的移动。这种复杂性源于多种因素，例如运输供需之间错综复杂的相互作用，以及开放、异构和适应性强的系统固有的随机性质。成功理解和驾驭这个系统是一个挑战。然而，随着城市交通和各个领域中多源数据的日益普及，以及大规模机器学习 （ML） 模型的最新进展，出现了一个巨大的机会。在本文中，我们介绍了一种新的概念框架，即 HUJ（分层统一 GEnerative）基础模型，以解决城市交通系统中嵌入的多方面计算任务和决策问题。我们深入研究了核心技术及其无缝集成以实现此框架，突出了它利用大量数据分析、分层 ML 方法和特定领域知识的潜力。该构想的框架有可能以数字化和智能化的方式彻底改变城市交通系统的规划、设计、建设和管理。