Augmented Betweenness Centrality for Mobility Prediction in Transportation Networks

Measuring and predicting the human mobility along the links of a transportation network has always been of a great importance to researchers in the field. Hitherto, producing such data relied heavily on expensive and time consuming surveying and on-field observational methods. In this work we propose an efficient estimation method for the assessment of the flow through links in transportation networks that is based on the Betweenness Centrality measure of the network’s nodes. Furthermore, we show that the correlation between those two features can be significantly increased when additional (pre-defined and known) properties of the network are taken into account, generating an augmented Mobility Oriented Betweenness Centrality measure. We validate the results using a transportation dataset, constructed using cellular phones data, that contains a high resolution network of the Israeli transportation system. We show that the flow that was measured using this expensive and complicated method can be accurately estimated using our proposed Augmented Betweenness technique.