Analysis of a mathematical model in Python for geographical disaggregation of freight growth rates based on the pivot-point method

Abstract: One of the tasks for the Swedish Transport Agency, Trafikverket, is to provide traffic forecasts. To do this, a number of different forecast models are used, where Samgods is a nationally estimated model, where the quality of the results gets more unstable the more disaggregated level you are looking at. For rail this is handled with a model called Bangods. However, in Bangods the difference in geographical growth within each commodity group is lost. This thesis examines whether it is possible to replace the national growth rates from Samgods with geographical disaggregated growth rates. The growth rates are calculated with a mathematical model based on the pivot point method (PPM). The model has been implemented in Python and is used to disaggregate the growth rates from Samgods to maintain the geographical growth. However, the data to the model comes from different systems and models that use different link formats. Therefore a link matching method is required that converts links from one system to another before using PPM. The growth rates from the PPM and the link matching-method has been modelled for twelve commodity groups, 8 or 1417 geographic regions and with or without a train division with four train types. The best result was to used 96 growth rates divided into twelve commodity groups and eight geographical regions.