Mathematical Evaluation of Passenger and Freight Rail Transport as Viewed Through the COVID-19 Pandemic and the War in Ukraine Situation

• Autorzy: Borucka Anna , Kozłowski Edward

Identyfikatory

DOI

10.12913/22998624/188503

• Języki publikacji: EN

Abstrakty

EN

The main objective of the study was to develop a method for identifying the com-ponents 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 in-dividual 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.

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 4
• Strony: 238--249
• Opis fizyczny: Bibliogr. 44 poz., fig., tab.

Twórcy

autor

Borucka Anna

• Faculty of Security, Logistics and Management, Military University of Technology, ul. Kaliskiego 2, 00-908 Warsaw, Poland

autor

Kozłowski Edward

• Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-7147-4903

Bibliografia

• 1. Sudan T., Taggar, R. Recovering supply chain disruptions in post-COVID-19 pandemic through transport intelligence and logistics systems: India’s experiences and policy options. Frontiers in Future Transportation 2021; 2: 660116.
• 2. Andrych-Zalewska M., Chlopek Z., Pielecha J., Merkisz J. Investigation of exhaust emissions from the gasoline engine of a light duty vehicle in the Real Driving Emissions test. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(2): 165880.
• 3. Murawski J., Szczepański E., Jacyna-Gołda I., Izdebski M., and Jankowska-Karpa D. Intelligent mobility: A model for assessing the safety of children traveling to school on a school bus with the use of intelligent bus stops. Eksploatacja i Niezawodność – Maintenance and Reliability 2022; 24(4): 695–706.
• 4. Oszczypała M., Ziółkowski J., Małachowski J. Semi-Markov approach for reliability modelling of light utility vehicles. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(2): 161859.
• 5. Kukulski J., Lewczuk K., Góra I., Wasiak M. Methodological aspects of risk mapping in multimode transport systems. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(1): 19.
• 6. Szaciłło L., Jacyna M., Szczepański E., Izdebski M. Risk assessment for rail freight transport operations. Eksploatacja i Niezawodność – Maintenance and Reliability 2021; 23(3): 476–488.
• 7. Zhang Y., Zhao M. An integrated approach to estimate storage reliability with masked data from series system. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(4): 172922.
• 8. Zhu G., Chou M.C., Tsai C.W. Lessons learned from the COVID-19 pandemic exposing the shortcomings of current supply chain operations: A long-term prescriptive offering. Sustainability 2020; 12(14): 5858.
• 9. Ciuffini F., Tengattini S., Bigazzi A.Y. Mitigating increased driving after the COVID-19 pandemic: an analysis on mode share, travel demand, and public transport capacity. Transportation Research Record: Journal of the Transportation Research Board 2023, 2677(4): 154–167.
• 10. Merk O., Hoffmann J., Haralambides H. Post-COVID-19 scenarios for the governance of maritime transport and ports. Maritime Economics & Logistic 2022; 24(4): 673–685.
• 11. Eisenmann C., Nobis, C., Kolarova V., Lenz B., Winkler C. Transport mode use during the COVID-19 lockdown period in Germany: The car became more important, public transport lost ground. Transport Policy 2021; 103: 60–67.
• 12. Shamshiripour A., Rahimi E., Shabanpour R., Mohammadian A.K. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspective 2020; 7: 100216.
• 13. Grechi D., Ceron M. COVID-19 lightening the load factor in railway transport: Performance analysis in the north-west area of Milan. Research in Transportation Business & Management 2022; 43: 100739. 14. Lucchesi S.T., Tavares V.B., Rocha M.K. Larranaga A.M., Public Transport COVID-19-Safe: New Barriers and Policies to Implement Effective Countermeasures under User’s Safety Perspective. Sustainability 2022; 14(5): 2945.
• 15. Kozłowski E., Borucka A., Oleszczuk P., Jałowiec T. Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(4): 172857.
• 16. Severo M., Ribeiro A.I., Lucas R., Leão T., Barros H. Urban rail transportation and SARS-Cov-2 infections: an ecological study in the Lisbon metropolitan area. Frontiers in Public Health 2021; 9: 611565.
• 17. Lu J., Lin A., Jiang C., Zhang A., Yang Z. Influence of transportation network on transmission heterogeneity of COVID-19 in China. Transportation Research Part C: Emerging Technologies 2021; 129: 103231.
• 18. Burdzik R., Speybroeck N. Study on the estimation of Sars-Cov-2 virus pathogens’ transmission probabilities for different public bus transport service scenarios. Transport Problems 2023; 18(3): 199–211.
• 19. Liu R., Li D., Kaewunruen, S. Role of Railway Transportation in the Spread of the Coronavirus: Evidence From Wuhan-Beijing Railway Corridor. Frontiers in Built Environment 2020; 6: 590146.
• 20. Wang Y., Fang Z., Gao W. Covid‐19’s impact on China’s economy: a prediction model based on railway transportation statistics. Disasters 2021; 45: 76–96.
• 21. Nguyen H.K. Application of mathematical models to assess the impact of the COVID-19 pandemic on logistics businesses and recovery solutions for sustainable development. Mathematics 2021; 9(16): 1977.
• 22. Arai Y., Niwa Y., Kusakabe T., Honma K. How has the Covid-19 pandemic affected wheelchair users? Time-series analysis of the number of railway passengers in Tokyo. Humanities and Social Sciences Communications 2023; 490.
• 23. Saxena A., Yadav A.K. Examining the Effect of COVID-19 on rail freight volume and revenue using the ARIMA forecasting model and assessing the resilience of Indian railways during the pandemic. Innovative Infrastructure Solutions 2022; 7: 348.
• 24. Kallidoni M., Katrakazas C., Yannis G. Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis Europiean Journal of Transport and Infrastructure Research 2022; 22(2): 161–182.
• 25. Liu Q., Huang Z. Research on intelligent prevention and control of COVID-19 in China’s urban rail transit based on artificial intelligence and big data. Journal of Intelligent & Fuzzy Systems 2020; 39(6): 9085–9090.
• 26. Asad S.M., Dashtipour K., Rais R.N.B., Hussain S., Abbasi Q.H., Imran M.A. Employing machine learning for predicting transportation modes under the COVID-19 pandemic: A mobility-trends analysis. In: International Conference on UK-China Emerging Technologies (UCET), Chengdu, China 2021; 235–240.
• 27. Rodríguez-Pérez R., Bajorath J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of Computer- Aided Molecular Design 2020; 34: 1013–1026.
• 28. Wooldridge M. Introductory Econometrics: A Modern Approach. Cengage learning, 2015.
• 29. Li K., Ren L., Li X., Wang Z. Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance. Eksploatacja i Niezawodność – Maintenance and Reliability 2024; 26(1): 174903.
• 30. Cleveland W.S., Susan J.D. Locally weighted regression: An approach to regression analysis by local fitting. Journal of American Statistical Association 1988; 83(403): 596–610.
• 31. Hamilton J.D. Time Series Analysis.Princeton University Press, 2020.
• 32. Borucka A, Kozłowski E, Oleszczuk P, Świderski A. Predictive analysis of the impact of the time of day on road accidents in Poland, Open Engineering 2020; 11(1): 142–150.
• 33. Holt C. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 2004; 20(1): 5–10.
• 34. Burdzik R. Epidemic Risk Analysis and Assessment in Transport Services: COVID-19 and Other Viruses. CRC Press, 2021.
• 35. Burdzik R. Probability of transmission of SARS-CoV-2 virus pathogens in long-distance passenger transport. Archives of Transport 2023; 68(4): 21–39.
• 36. Jaroń A. Borucka, A., Parczewski R. Analysis of the impact of the COVID-19 pandemic on the value of CO2 emissions from electricity generation. Energies 2022; 15: 4514.
• 37. Burdzik R. An Application of the DHI Methodology for a Comparison of SARS-CoV-2 Epidemic Hazards in Customer Delivery Services of Smart Cities. Smart Cities 2023; 6: 965–986.
• 38. Office of Rail Transport (UTK), railway data 2012–2023, UTK statistical portal, https://utk.gov.pl/
• 39. Zhang C., Zhang Y., Dui H., Wang S., Tomovic M. Component maintenance strategies and risk analysis for random shock effects considering maintenance costs. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(2): 162011.
• 40. Niewczas A., Mórawski Ł., Rymarz J., Dębicka E., Hołyszko P. Operational risk assessment model for city buses. Eksploatacja i Niezawodność – Maintenance and Reliability 2023; 25(1): 14.
• 41. Benmalek E., Elmhamdi J., Jilbab A., Jbari A. A cough-based Covid-19 detection with gammatone and mel-frequency cepstral coefficients. Diagnostyka 2023; 24(2): 2023214.
• 42. Bensid K., Lati A., Benlamoudi A., Ghouar B.E., Senoussi M.L. Efficient Covid-19 disease diagnosis based on cough signal processing and supervised machine learning. Diagnostyka 2023; 24(1): 2023103.
• 43. Rybak G., Kozłowski E., Król K., Rymarczyk T., Sulimierska A., Dmowski A., Bednarczuk, P. Algorithms for optimizing energy consumption for fermentation processes in biogas Production. Energies 2023; 16: 7972.
• 44. Pluciński M., Kotowska I., Mańkowska M., Filina-Dawidowicz L. Research on diversification strategies of terminal operators – evidence from Polish Seaports. Sustainability 2023; 15: 5644.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).