论文标题
估计自行车共享系统中潜在网络流
Estimation of Latent Network Flows in Bike-Sharing Systems
论文作者
论文摘要
在统计网络分析中，对潜在网络流的估计是一个常见的问题。典型的设置是我们知道网络的边缘，即内在和超级目标，但是流量没有观察到。在本文中，我们开发了一个混合回归模型，以估计自行车共享网络中的网络流量，如果知道自行车站的小时差异和超级差异。我们还包括外源协变量，例如天气条件。该模型的两个不同参数化估计1）整个网络流量和2）仅网络边缘。模型参数的估计是通过迭代惩罚的最大似然方法提出的。通过对维也纳自行车共享网络中的网络流进行建模来说明这一点。此外，进行了一项模拟研究以显示模型的性能。就实际目的而言，预测何时何时和在哪个站点缺乏或过量自行车是至关重要的。对于此应用程序，我们的模型通过提供相当准确的预测表明非常适合。
Estimation of latent network flows is a common problem in statistical network analysis. The typical setting is that we know the margins of the network, i.e. in- and outdegrees, but the flows are unobserved. In this paper, we develop a mixed regression model to estimate network flows in a bike-sharing network if only the hourly differences of in- and outdegrees at bike stations are known. We also include exogenous covariates such as weather conditions. Two different parameterizations of the model are considered to estimate 1) the whole network flow and 2) the network margins only. The estimation of the model parameters is proposed via an iterative penalized maximum likelihood approach. This is exemplified by modeling network flows in the Vienna Bike-Sharing Network. Furthermore, a simulation study is conducted to show the performance of the model. For practical purposes it is crucial to predict when and at which station there is a lack or an excess of bikes. For this application, our model shows to be well suited by providing quite accurate predictions.
