论文标题
关于在非平衡学习下交通网络的弹性
On the Resilience of Traffic Networks under Non-Equilibrium Learning
论文作者
论文摘要
我们研究了基于学习的\ textIt {智能导航系统}（INS）的弹性，以利用IT基础架构的漏洞并操纵交通状况数据。为此，我们提出了\ textit {Wardrop non-equilibrium solution}（WANES）的概念，该概念捕获了学习过程中动态交通流动适应的有限时间行为。提出的以目标集和测量功能为特征的非平衡解决方案评估了在有界数的相互作用数量下学习的结果，并且与近似平衡的概念有关。利用有限时间分析方法，我们发现在镜下降（MD）在线学习框架下，交通流量轨迹能够在有限的INS攻击后能够恢复到Wardrop非平衡解决方案。由此产生的性能损失是$ \ tilde {\ mathcal {o}}（t^β）$（$ - - \ frac {1} {2} {2} \ leqβ<0）$），其恒定取决于交通网络的大小，表明基于MD的INS的弹性。我们使用SIOUX-FALL运输网络上的疏散案例研究来证实结果。
We investigate the resilience of learning-based \textit{Intelligent Navigation Systems} (INS) to informational flow attacks, which exploit the vulnerabilities of IT infrastructure and manipulate traffic condition data. To this end, we propose the notion of \textit{Wardrop Non-Equilibrium Solution} (WANES), which captures the finite-time behavior of dynamic traffic flow adaptation under a learning process. The proposed non-equilibrium solution, characterized by target sets and measurement functions, evaluates the outcome of learning under a bounded number of rounds of interactions, and it pertains to and generalizes the concept of approximate equilibrium. Leveraging finite-time analysis methods, we discover that under the mirror descent (MD) online-learning framework, the traffic flow trajectory is capable of restoring to the Wardrop non-equilibrium solution after a bounded INS attack. The resulting performance loss is of order $\tilde{\mathcal{O}}(T^β)$ ($-\frac{1}{2} \leq β< 0 )$), with a constant dependent on the size of the traffic network, indicating the resilience of the MD-based INS. We corroborate the results using an evacuation case study on a Sioux-Fall transportation network.
