高阶交互揭示骑行基础设施网络的协同骨干结构

中文摘要 AI 辅助

基础设施网络从根本上支撑着人类流动与交通。提升单条链路的品质能在局部改善网络性能。然而，高效交通需要跨越多条链路的高质量连通走廊，而这并非源于独立的单链路升级。在此，我们引入一个框架，将多条链路的联合升级评估为固有的高阶交互，从而能够量化复杂交通网络中的链路协同效应。两条链路若具有协同性，则升级其中一条会增加升级另一条的收益，从而促进沿同一路径上拓扑互补链路的升级，同时抑制冗余平行链路的升级。通过将这些协同效应表达为整体网络性能的二阶导数，我们开发了一个高效的计算框架，以识别形成连通网络骨干的协同链路。我们结合德国汉堡的经验街道网络与骑行需求数据，以及一个针对城市自行车交通的扰动效用路径选择模型，应用了我们的理论框架。我们的结果揭示了高阶交互产生的协同效应，从而能够实现超越复杂交通与流动网络中局部链路重要性的战略性基础设施规划。

英文摘要

Infrastructure networks essentially underlie human mobility and transport. Improving the quality of single links increases network performance locally. However, efficient transport requires high-quality connected corridors across multi-link paths that do not emerge from independent single-link upgrades. Here, we introduce a framework for evaluating the impact of jointly upgrading multiple links as inherently higher-order interactions, enabling us to quantify link synergies in complex transport networks. Two links are synergistic if an upgrade of one increases the benefit of upgrading the other, promoting upgrades of topologically complementary links along the same path while discouraging upgrades of redundant parallel links. By expressing these synergies as second-order derivatives of overall network performance, we develop an efficient computational framework to identify synergistic links that form a connected network backbone. We apply our theoretical framework by combining empirical street network and cycling demand data for Hamburg, Germany, with a perturbed utility route choice model for urban bicycle traffic. Our results reveal synergies from higher-order interactions, thereby enabling strategic infrastructure planning that goes beyond local link importance in complex transport and flow networks.

发表机构

• TUD Dresden University of Technology（德累斯顿工业大学）
• AMOLF（阿莫夫研究所）

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