Transit ridership forecasting at station level: an approach based on distance-decay weighted regression

This article develops a rapid response ridership forecast model, based on the combined use of Geographic Information Systems (GIS), distance-decay functions and multiple regression models. The number of passengers boarding at each station in the Madrid Metro network is estimated as a function of the characteristics of the stations (type, number of lines, accessibility within the network, etc.) and of the areas they serve (population and employment characteristics, land-use mix, street density, presence of feeder modes, etc.). The paper considers the need to evaluate the distance threshold used (not the choice of a fixed distance threshold by assimilation from other studies), the distance calculation procedure (network distance versus straight-line distance) and, above all, the use of distance-decay weighted regression (so that the data from the bands nearer the stations have a greater weighting in the model than those farther away). Analyses carried out show that weighting the variables according to the distance-decay functions provides systematically better results. The choice of distance threshold also significantly improves outcomes. When an all-or-nothing function is used, the way the service area is calculated (straight-line or network distances) does not seem to have a decisive influence on the results. However, it seems to be more influential when distance-decay weighting is used.

► The article develops a rapid response ridership forecast model. ► It is based on the use of distance-decay functions and multiple regression models. ► Weighting the predictors according to that functions provides better results.