Application of geographically weighted regression to the direct forecasting of transit ridership at station-level

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How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas (2018, methodology): “Other network indicators such as the betweenness centrality or the node degree mentioned 240 by Cats (2016) could have been used, however due to a lack of digital representations of the 48 city 241 networks this was not possible.”