论文标题
经常流动网络：用于城市流动性密度建模的经常性潜在变量模型
Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility
论文作者
论文摘要
按需移动性（MOD）系统代表了一种快速发展的运输方式，其中旅行请求由协调的车辆动态处理。至关重要的是，MOD系统的效率在很大程度上取决于供求分布在时空空间中如何对齐（即，为了满足用户需求，必须在正确的位置和所需的时间可用汽车）。为此，我们认为预测模型应旨在在城市流动需求的进化中明确散布时间和空间变化。但是，当前的方法通常通过共同处理两个可变性来源，或者完全忽略它们的存在来忽略这种区别。在本文中，我们提出了复发性流动网络（RFN），在其中探讨了（i）在复发性神经网络隐藏状态中包含（i）潜在的随机变量以建模时间变异性，以及（ii）将流量归一化以建模流动性需求的空间分布。我们展示了空间变异性和时间变异性之间明确散布的预测模型如何表现出多种理想的特性，并从经验上展示了如何使分布的产生匹配潜在的复杂城市拓扑。
Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles. Crucially, the efficiency of an MoD system highly depends on how well supply and demand distributions are aligned in spatio-temporal space (i.e., to satisfy user demand, cars have to be available in the correct place and at the desired time). To do so, we argue that predictive models should aim to explicitly disentangle between temporal} and spatial variability in the evolution of urban mobility demand. However, current approaches typically ignore this distinction by either treating both sources of variability jointly, or completely ignoring their presence in the first place. In this paper, we propose recurrent flow networks (RFN), where we explore the inclusion of (i) latent random variables in the hidden state of recurrent neural networks to model temporal variability, and (ii) normalizing flows to model the spatial distribution of mobility demand. We demonstrate how predictive models explicitly disentangling between spatial and temporal variability exhibit several desirable properties, and empirically show how this enables the generation of distributions matching potentially complex urban topologies.
