Utveckling av en prognosmodell för kollektivtrafik i mindre städer

It has become more important for operators to be able to predict the future number of public transport passengers when consider to place a tender for operating public transport in a city or region, this is due to the new types of operator contracts was introduced quite recently. There are models in use today that can predict this, but they are often time consuming and complex and therefore it can be expensive to perform a forecast. Aside from this, most models in use for Sweden today are adapted for larger cities. Thus, the aim of this thesis is to propose a model that requires minimal input data with a short set up and execution time that can be used to predict a forecast for the public transport system in smaller cities without notably affecting the quality of the result. The developed model is based on a forecast model called LuTrans, which in turn is based on a common method, the four step model. The aim of the model lies within public transportation but it also consider other modes. The input data used by the model mainly consists of socio-economic data, the travel time and distance between all the zones in the network. The model also considers the cost for traveling by car or public transport. The developed model was applied to the Swedish city, Örebro, where a forecast was conducted for a future scenario. It is easily to apply the model to different cities to estimate a forecast for the public transport system. The developed model for the base scenario predicts trips for individual bus lines with an accuracy of 85 % for the city of Örebro. The developed model gave the result that the trips made by public transport in the future scenario of Örebro 2025 will increase annually by 0.94 %. The conclusion is that it is possible to develop a simple model that can be easily applied for a desired city. Although the developed model produced a plausible result for Örebro, further work such as implementation on other cities are required in order to fully evaluate the developed model