Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/335827 
Year of Publication: 
2024
Citation: 
[Journal:] Economic Themes [ISSN:] 2217-3668 [Volume:] 62 [Issue:] 1 [Year:] 2024 [Pages:] 1-17
Publisher: 
Paradigm Publishing Services, Warsaw
Abstract: 
As the backbone of environmentally sustainable transport, rail transport is one of the most preferred modes since it emits three times less CO2 and particulates per ton-mile than road transport. Besides these ecological benefits, rail transport is the most costeffective. The global energy crisis creates significant problems and challenges for rail companies when planning transportation activity costs. Companies must carefully consider energy spending and ways to decrease it. In this paper, the authors considered the problem of predicting freight train energy consumption to help companies plan their budgets. For that purpose, the authors applied three time series methods: the moving average, the weighted moving average, and the exponential smoothing method. These methods were applied to actual data collected in the Republic of Serbia. The results showed that the exponential smoothing method performs better than the other two approaches. Nevertheless, there is still room for improvement in the presented approaches, such as fine-tuning the parameters used and comparing them to other relevant techniques used for the forecast.
Subjects: 
Freight train
energy consumption
time series models
forecast
JEL: 
C32
R40
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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