Application of geographically weighted regression to the direct forecasting of transit ridership at station-level
mode - subway/metro, place - europe, ridership - forecasting, technology - geographic information systems
Transit ridership, Direct forecasting models, Geographically weighted regression (GWR), Geographic information systems (GIS), Metro, Madrid
In recent years, station-level ridership forecasting models have been developed based on Geographic Information Systems (GIS) and multiple regression analysis. These models estimate the number of passengers boarding at each station as a function of the station characteristics and the areas that they serve. These models have considerable advantages over the traditional four-step model, including simplicity of use, easy interpretation of results, immediate response and low cost. Nevertheless, the models usually use traditional ordinary least squares (OLS) multiple regression, which assume parametric stability. This study proposes a direct model that uses geographically weighted regression (GWR) to forecast boarding at the Madrid Metro stations. Here, the results obtained using the OLS and GWR models are compared. The GWR model results in a better fit than the traditional one. In addition, the information supplied by the GWR model regarding the spatial variation of elasticities and their statistical significance provides more realistic and useful results.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Cardozo, O.D., García-Palomares, J.C., & Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, Vol. 34, pp. 548-558.