AI 中文总结
该研究引入数据驱动框架，用几何比较和最优传输理论，比较自组织、系统最优及实际地铁网络。以新加坡地铁系统为例，发现实际网络更接近自组织基准，表明仅系统最优性不足以指导干预，框架可跨地区支持适应性规划。
AI 中文摘要
城市交通网络设计通常通过基于工程和经济学的自上而下规划来进行。然而城市是复杂系统，基础设施与出行需求之间存在反馈回路：网络结构塑造起讫点（OD）流，而OD流又会适应并影响网络发展。尽管存在这种相互依存关系，但用于根据替代生成原则对现实世界网络进行基准测试的计算工具仍然有限。在此，我们引入一个数据驱动框架来比较三种类型的地铁网络：源自局部规则的自组织（愿望路径）网络、源自全局优化的系统最优网络以及实际的现实世界网络。我们使用几何比较和最优传输理论来量化相似性和差异。将该框架应用于新加坡的地铁系统，我们发现实际地铁网络与自组织基准的接近程度远高于系统最优基准，剩余差异主要由地理限制解释。这些发现凸显仅靠系统范围的最优性可能不足以指导实际干预措施，这促使规划方法明确纳入当地服务需求。该框架可跨地区转移，能够诊断设计与使用的不一致，以支持适应性基础设施规划。
英文摘要
Urban transportation network design is typically approached through top-down planning grounded in engineering and economics. Yet cities are complex systems characterized by feedback loops between infrastructure and mobility demand: network structure shapes origin-destination (OD) flows, while OD flows adapt and in turn affect network development. Despite this interdependence, computational tools to benchmark real-world networks against alternative generative principles remain limited. Here, we introduce a data-driven framework to compare three types of metro networks: a self-organized (desire-path) network derived from local rules, a system-optimal network derived from global optimization, and the empirical real-world network. We quantify similarities and discrepancies using geometric comparisons and optimal transport theory. Applying the framework to Singapore's Mass Rapid Transit system, we find that the empirical metro network is substantially closer to the self-organized benchmark than to the system-optimal benchmark, with remaining discrepancies largely explained by geographic constraints. These findings highlight that system-wide optimality alone may be inadequate for guiding practical interventions, motivating planning approaches that explicitly incorporate local service needs. The framework is transferable across regions and can diagnose design-use misalignments to support adaptive infrastructure planning.
Journal refComputers, Environment and Urban Systems (2026) 129: 102479
DOI：10.1016/j.compenvurbsys.2026.102479
