Bogna Mrówczyńska

A comparison of forecasting the results of road transportation needs

Abstract Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winters method for seasonal problems – a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error)...

Assessment of Polish Railway Infrastructure and the Use of Artificial Intelligence Methods for Prediction of Its Further Development

The aim of this chapter is to analyze economic trends in rail freight volume in Poland, based on the analysis of data from the years 2009–2013, for evaluating decisions on planned investments in railway infrastructure envisioned by Poland and the EU at the time the EU was founded. The theoretical analysis presents a trend of functional Polish railways and its impact on investment decisions. In addition, it shows the long-term plans for railway transport in Poland from both the Polish government and the EU perspectives. An analysis of the current investment to support the development of railways in Poland is also elaborated. The research part of the chapter presents an analysis of statistical data on rail freight. Forecasts are precisely presented of selected transport parameters made by the Bayesian network method and Holt-Winters double exponential smoothing using an artificial immune system to determine parameters and initial conditions.