Modelling Dependencies Between Passenger Numbers and Selected Parameters Characterizing the Railway Station and Its Accessibility Using the NOAH Algorithm

Amid the well-researched negative effects of road congestion and increased private car use, there is a need for more sustainable modes of transport. The literature points towards trains as being a vital part of the solution to the current problems, but their success depends on a number of variables, especially when it comes to the main railway stations in the largest cities. The first goal of this study was to identify the relationship between passenger numbers at major railway stations in Poland and selected parameters characterizing public transport services; the second was to assess the usefulness of the NOAH (Nest of Apes Heuristic) method for data analysis. In Poland, the number of major transfer hubs is limited, and there is a lack of an existing method allowing comparison of variables in such small datasets in a way that infers statistical significance. This is a research gap that the authors aimed to address using the NOAH algorithm combined with an analysis of regression. The initial dataset had been successfully expanded in a way that dependencies could be observed, with both goals being met. Passenger numbers relied most on the number of trains departing at each station daily, while walking distance during transfers impacted that number most negatively. The results point towards other variables influencing the passenger numbers, which were not considered in this study but could form the basis of further research. The utilized method could also be applied to a different group of cities, and in other countries. Additionally, the study added to the development of the NOAH algorithm itself, improving the method.