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.
翻译:需要时流动(MoD)系统代表着一种迅速发展的运输方式,即旅行申请由协调的车队以动态方式处理。关键是,国防部系统的效率高度取决于在时空空间供求分配如何一致(即为了满足用户需求,汽车必须在正确的地点和理想的时间提供)。为了做到这一点,我们认为,预测模型应旨在明确区分时间}和城市流动需求变化的空间变异性。然而,目前的方法通常忽视这一区别,要么共同处理变异性源,要么完全忽略其存在。我们在此文件中提议经常性流动网络(RFN),我们在此探索(i) 经常性神经网络隐藏状态的潜在随机变量,以模拟时间变异性,以及(ii) 将流动需求的空间分布模式的正常化。我们展示了预测模型如何明显地显示空间与时间变异性之间的几种可取性,并用经验显示这如何使得能够生成与潜在复杂城市地形相匹配的分布。