城市交通网络设计通常采用基于工程学与经济学的自上而下规划方法。然而，城市是复杂的系统，其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流又随之适应并反作用于网络演化。尽管存在这种相互依赖关系，目前仍缺乏可将现实网络与替代性生成机制进行基准比对的计算工具。本文提出一种数据驱动框架，用于比较三类地铁网络：由局部规则衍生的自组织（愿望路径）网络、由全局优化导出的系统最优网络，以及实证观测的真实世界网络。我们借助几何比较与最优传输理论量化其相似性与差异性。将该框架应用于新加坡大众捷运（MRT）系统，发现实证地铁网络与自组织基准的接近程度显著高于其与系统最优基准的接近程度；残余差异主要可由地理约束解释。这些结果表明，仅以系统层面最优性为指导可能不足以支撑实际干预措施，因而有必要发展明确纳入本地服务需求的规划方法。该框架具有跨区域可迁移性，可用于诊断网络设计与使用之间的错配，从而支持适应性基础设施规划。

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.