Leite Daniela, De Bacco Caterina
Max Planck Institute for Intelligent Systems, Cyber Valley, Tübingen, Germany.
Nat Commun. 2024 Sep 12;15(1):7981. doi: 10.1038/s41467-024-52313-6.
Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch. Analyzing a set of urban train, tram and subway networks, we find a noteworthy degree of similarity in several of the studied cases between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different subway, tram and train networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks.
设计和优化城市交通网络结构是一项具有挑战性的任务。在本研究中，我们提出了一种受最优传输理论和规模经济原则启发的方法，该方法在输入中使用很少的信息来生成与公共交通网络结构相似的结构。与标准方法相反，它不假设任何初始骨干网络基础设施，而是仅使用几个起点和终点直接从连续空间中提取此基础设施，从头开始生成网络。通过分析一组城市火车、电车和地铁网络，我们发现在几个研究案例中，模拟基础设施与实际基础设施之间存在显著程度的相似性。通过调整一个参数，我们的方法可以模拟一系列不同的地铁、电车和火车网络，这些网络可进一步用于就相关交通特性提出可能的改进建议。我们算法的输出自然地提供了两个网络之间相似性的有原则的定量度量，可用于自动选择相似的模拟网络。
