发表机构
School of Civil Engineering, University of Sydney（悉尼大学土木工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对公交系统客流随机性问题，扩展单线模型并提出常规公交与灵活公交混合模型，采用连续近似方法，发现容量随随机性增加及混合模型二元状态，为自适应高效公交设计提供依据。
AI 中文摘要
在全球大都市地区，公共交通受益于公交系统。公交设计广泛采用理论模型，这些模型通常假设客流量是静态的。然而，客流量存在随机性，忽视这一点可能导致错误的设计决策。超越静态客流，我们将传统单线模型扩展至随机客流情形。进一步地，为在多种客流随机性下优化公交系统，我们引入了一种结合常规公交（CBs）和灵活公交（FBs）的混合模型。在这两种模型中，我们应用连续近似方法并发现：1）在扩展的单线模型中，公交容量随客流随机性增加而增大；2）混合模型表现出二元状态：在低客流随机性下，常规公交提供服务并伴随拒载，或在高客流随机性下，常规公交服务多数乘客，而灵活公交服务少数乘客并伴随拒载。我们的发现建立了公交系统设计与客流随机性之间的联系，有助于构建更具适应性和效率的公共交通框架。
英文摘要
In global metropolitan areas, public transport benefits from bus systems. Bus design widely applies theoretical models, which typically assume static ridership. However, ridership randomness exists, and lack of attention to it might lead to wrong design decisions. Moving beyond static ridership, we extend the traditional single-line model with stochastic ridership. Further, to optimize the bus system under various ridership randomness, we introduce a hybrid model that combines conventional buses (CBs) and flexible buses (FBs). In both models, we apply a continuous approximation approach and find that: 1) Bus capacity increases with ridership randomness in the extended single-line model; 2) The hybrid model exhibits a binary state: either CBs serve the ridership with rejections under low ridership randomness, or CBs serve the majority while FBs serve the minority with rejections under high ridership randomness. Our findings establish a link between bus system design and ridership randomness, contributing to a more adaptive and efficient public transport framework.
