Abstract
Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.
Highlights
Rail transport capability is a critical indicator of the competitiveness of a country’s economy and the possibilities of its development, which is why it is important to carry out analyses to assess the functioning and development of this mode of transport and to indicate possible and directions of change and expansion (Konowrocki, Chojnacki 2020; Kang et al 2019; Markovits-Somogyi 2011; Baležentis, A., Baležentis, T. 2011)
We analysed a set of transport performance data for passenger rail transport in Poland and compared different models to select one with the best prediction accuracy
A comparative analysis of the data presented in Table 5 and Figure 6 shows that the data set considered in this paper is best described by the AutoRegressive Integrated Moving Average (ARIMA) model ((1, 0, 0)
Summary
Rail transport capability is a critical indicator of the competitiveness of a country’s economy and the possibilities of its development, which is why it is important to carry out analyses to assess the functioning and development of this mode of transport and to indicate possible and directions of change and expansion (Konowrocki, Chojnacki 2020; Kang et al 2019; Markovits-Somogyi 2011; Baležentis, A., Baležentis, T. 2011). Effective determination of future transport performance allows to increase energy efficiency and reduce harmful emissions in the entire transport system by effectively planning the use of accompanying infrastructure or other, related types of transport This issue was analysed, among others, by Jarašūnienė et al (2019), who demonstrated that efficient interoperability of railway and maritime transport depended, among others, on coordination of large numbers of participants, strict schedule observance, and processing of large amounts of information. They found that the most important of these factors was the development of an effective information system by integrating individual elements and data regarding rail and sea transport. This shows that expert systems, including integrated systems, need to be supported in effective planning of transport performance
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