公共交通网络在路线攻击下的鲁棒性研究
Robustness of the public transport network against attacks on its routes
AI总结：
本研究针对公交线路攻击，评估公共交通网络鲁棒性，发现基于介数中心性的攻击和一步最大危害策略最有效，且介数策略使网络分裂为规模相近的组件。
摘要：
我们研究了公共交通网络（PTNs）在遭受路线攻击时的鲁棒性，特别关注公共汽车线路。此类攻击反映了现实世界中的情景，为理解城市的多方面动态提供了洞见。我们的研究深入探讨了基于中心性度量策略系统性移除整条路线的后果。通过分析碎片化网络的规模，我们评估了网络的鲁棒性，重点关注最大连通分量及其衍生指标。为评估各种攻击策略的有效性，我们将这些策略应用于一个合成PTN模型和一个真实世界实例——阿根廷布宜诺斯艾利斯都会区。我们将这些策略与随机攻击、一步最大危害和一步最小危害程序进行了对比。研究结果表明，基于介数中心性的攻击和一步最大（最大）危害程序是最有效的攻击策略。值得注意的是，介数中心性策略将网络分割成规模相近的多个连通分量，而其他方法则产生一个主导性分量和若干次要分量。
英文摘要：
We investigate the robustness of Public Transport Networks (PTNs) when subjected to route attacks, focusing specifically on public bus lines. Such attacks, mirroring real-world scenarios, offer insight into the multifaceted dynamics of cities. Our study delves into the consequences of systematically removing entire routes based on strategies that use centrality measures. We evaluate the network's robustness by analyzing the sizes of fragmented networks, focusing on the largest components and derived metrics. To assess the efficacy of various attack strategies, we employ them on both a synthetic PTN model and a real-world example, specifically the Buenos Aires Metropolitan Area in Argentina. We examine these strategies and contrast them with random, and one-step most and least harmful procedures. Our findings indicate that \textit{betweenness}-based attacks and the one-step most (\textit{maximal}) harmful procedure emerge as the most effective attack strategies. Remarkably, the \textit{betweenness} strategy partitions the network into components of similar sizes, whereas alternative approaches yield one dominant and several minor components.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
