The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.
翻译:人类流离失所在交通堵塞、隔离和流行病蔓延等复杂的社会现象中越来越关键的作用正在吸引来自若干学科的科学家的兴趣。在本条中,我们讨论了移动网络的生成,即产生一个城市的整个移动网络,这是一个加权定向图,其中节点是地理位置和加权边缘,代表了人们在这些地点之间的移动,从而描述了城市内整个流动的流量。我们的解决方案是MOGAN,这是一个基于Generation Aversarial Networks(GANs)的模型,以产生现实的移动网络。我们对自行车和出租车搭载的公共数据集进行了广泛的实验,以显示移动网络的生成网络在真实性方面超过了古典重力和辐射模型。我们的模型可用于数据扩充和进行模拟以及什么分析。