Mathematical Evaluation of Passenger and Freight Rail Transport as Viewed Through the COVID-19 Pandemic and the War in Ukraine Situation
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Borucka Anna, Kozłowski Edward |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2024 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 18 |
| Strony: | 238 - 249 |
| Impact Factor: | 1,0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus | BazTech | Google Scholar |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 20 czerwca 2024 |
| Abstrakty: | angielski |
| The main objective of the study was to develop a method for identifying the components of a time series disrupted by crisis events, allowing for evaluation, comparison and short-term forecasting of the impact of such situations. The article, using mathematical modelling, analyses and evaluates the impact of the pandemic and war on rail transport using Poland as an example. Three methods of local trend matching were used: locally estimated trend, locally estimated trend with seasonal components, locally estimated trend with locally estimated seasonal components. The study showed that the effects of the crisis are still felt today, but their impact on individual types of transport varied, and what’s more, differences were also visible depending on the item being transported. Passengers, despite the introduction of high sanitary standards, severely limited their mobility. The freight transport market turned out to be more resistant to the impact of the pandemic, but both the pandemic and the war in Ukraine affected the volume of goods transported. The article presents a method that allows to identify time series disturbed by crisis events and therefore difficult to describe and make reliable forecasts. The method proposed by the authors is an innovative answer to the problems of identifying very variable series and can also be used in the analysis of other issues in which time series have been disturbed in a sudden and unexpected way |
