Statistical Modelling 22 (4) (2022), 349–378
Estimation of latent network flows in bike-sharing systems
Marc Schneble,
Department of Statistics,
Faculty of Mathematics,
Informatics and Statistics,
Ludwig-Maximilians-Universität München,
Munich,
Germany.
e-mail: marc.schneble@stat.uni-muenchen.de
Göran Kauermann,
Department of Statistics,
Faculty of Mathematics,
Informatics and Statistics,
Ludwig-Maximilians-Universität München,
Munich,
Germany.
Abstract:
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, that is, in- and outdegrees, but the flows are unobserved. In this article, 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 (a) the whole network flow and (b) the network margins only. The estimation of the model parameters is proposed via an iterative penalized maximum likelihood approach. This is exemplified by modelling network flows in the Vienna bike-sharing system. In order to evaluate our modelling approach, we conduct our analyses exploiting different distributional assumptions while we also respect the provider's interventions appropriately for keeping the estimation error low. 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.
Keywords:
approximate EM-algorithm, Bike-sharing networks, generalized additive mixed models, network flow inference, skellam distribution
Downloads:
R Code and examples in zipped archive (contains file README.txt).
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