PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Forecasting the number of vehicle kilometers by applying the autoregression model, using Warsaw trams as an example

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the biggest challenges of the 21st century is ecological responsibility. It also concerns the sustainable development of transport and the reduction of threats related to the negative impact of this phenomenon on the environment. A constant increase in transport congestion, atmospheric air pollution, and noise promotes the search for new solutions, especially in urban areas. One of the systematically implemented and improved ideas in this area is the development of urban transport systems. Their effectiveness and efficiency are evidenced by the level of meeting the transport needs of residents, with the optimal utilization of vehicles. The article analyses urban transport in Warsaw, focusing only on trams as the second most popular means of transport after wheeled vehicles. Two objectives of the study were adopted. The first was evaluating the current state and characteristics of the available options and indicating potential development directions, considering factors that determine it. The second goal was to select the appropriate model describing the number of vehicle kilometers accumulated by Warsaw trams in the years 2017-2019 and parametric identification of this model. The study allowed us to estimate and make a short-term forecast of transport services carried out by trams. The research has shown that the current situation regarding the performance of transport work by trams in Warsaw does not fit into the paradigm of sustainable transport development. This is due to the loss of vehicles from the records in the absence of new vehicle purchases. Additionally, the developed tool indicates a decrease in the number of vehicles-km performed in the following months and, thus, a reduction in the share of trams in transport in the Warsaw communication system. The identified problem (i.e., a downward trend in transport performance) is essential from the point of view of the quality of the system's operation and the ability to meet passengers' expectations. It also informs decision-makers about the need to implement changes leading to an increase in the share of tram transport, mainly in capacity and operating costs.
Rocznik
Tom
Strony
83--93
Opis fizyczny
Bibliogr. 34 poz., tab., wykr.
Twórcy
  • Faculty of Security, Logistics and Management, Military University of Technology
  • Faculty of Security, Logistics and Management, Military University of Technology
  • Motor Transport Institute
Bibliografia
  • 1. Gołda, I. & Gołębiowski, P. & Izdebski, M. & et al. (2017). The evaluation of the sustainable transport system development with the scenario analyses procedure. Journal of Vibroengineering. Vol. 19. No. 7, 5627-5638.
  • 2. Mugion, R. G. & Toni, M. & Raharjo, H. & et al. (2018). Does the service quality of urban transport enhance sustainable mobility? Journal of Cleaner Production. Vol. 174, 1566-1587.
  • 3. Zhao, Z. & Chen, W. & Wu, X. & et al. (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems. Vol. 11.2, 68-75.
  • 4. Duraku, R. & Atanasova, V. (2017). Traffic volume forecast using regression analysis and artificial neural network based on principal components. Mechanics Transport Communications. Vol. 16. No. 3.1, 68-75.
  • 5. Borucka, A. (2020). Logistic regression in modeling and assessment of transport services. Open Engineerin. Vol. 10. No. 1, 26-34.
  • 6. Cyril, A. & Mulangi, R. H. (2019). Bus passenger demand modelling using time-series techniques-big data analytics. The Open Transportation Journal. Vol.13. No. 1, 41-47.
  • 7. Chistik, O. F. & Nosov, V. V. & Tsypin, A. P., & et al. (2016). Research indicators of railway transport activity in time series. Journal of Economics & Management Perspectives. Vol. 10. No. 3, 57-65.
  • 8. Pang, X. & Wang, C. & Huang, G.(2016). A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm. Journal of Transportation Technologies. Vol. 6, 200-206.
  • 9. Świderski, A. & Borucka, A. & Skoczyński, P. (2018). Characteristics and assessment of the road safety level in Poland with multiple regression model. In: Transport Means', Proceedings of the 22nd International Scientific Conference, Part I. Lithuania.
  • 10. Borucka, A. & Pyza, D. (2021). Influence of meteorological conditions on road accidents. A model Indexed by: for observations with excess zeros. Eksploatacja i Niezawodnosc – Maintenance and Reliability. Vol. 23. No. 3, 586-592.
  • 11. Borucka, A. (2018). Three-state Markov model of using transport means. Business Logistics In Modern Management, 3-19.
  • 12. Jia, Y. & Wu, J. & Xu, M. (2017). Traffic flow prediction with rainfall impact using a deep learning method. Journal of advanced transportation. Vol. 2017, 1-10.
  • 13. Hau, L. F. & Junior, J. C. V. & Ribeiro, P. & Quandt, V. I. (2019). Data Collection and Prediction of Urban Transport Flow using Neural Networks. International Journal of Advanced Engineering Research and Science. Vol. 6. No. 6, 476-483.
  • 14. Wang, X. & Zhang, N. & Zhang, Y. & Shi, Z. (2018). Forecasting of short-term metro ridership with support vector machine online model. Journal of Advanced Transportation. Vol. 2018, 1-10.
  • 15. Vidya, G. S. & Hari, V. S. & Shivasagaran, S. (2020). Estimation of Passenger Flow in a Bus Route using Kalman Filter. In: Proceedings of the 6th International Conference on Advanced Computing and Communication Systems. Coimbatore.
  • 16. Li, L. & Qin, L.& Qu, X. & et al. (2019). Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowledge-based Systems. Vol. 172, 1-14.
  • 17. Sun, S. & Li, Y. & Wang, X. & et al. (2017). Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transportation Research Part C: Emerging Technologies. Vol. 77, 306-328.
  • 18. Gu, Y. & Lu, W. & Xu, X. & et al. (2019). An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning. IEEE Transactions on Intelligent Transportation Systems. Vol. 21. No. 3, 1332-1342.
  • 19. Wang, X. & Zhang, N. & Chen, Y. & Zhang, Y. (2018). Short-term forecasting of urban rail transit ridership based on ARIMA and wavelet decomposition. In: Proceedings of 6th International Conference on computer-aides design, manufacturing, modeling and simulation, Busan.
  • 20. Li, J. & Wang, Z. & Liu, C. & Qiu, M. (2019). Accelerated degradation analysis based on a random-effect Wiener process with one-order autoregressive errors. Eksploatacja i Niezawodnosc – Maintenance and Reliability. Vol. 21. No.2. 264-255.
  • 21. Kozłowski, E. (2015). Analiza i identyfikacja szeregów czasowych, 1st ed. Lublin: Politechnika Lubelska.
  • 22. Huber, F. & Feldkircher, M. (2018). Adaptive Shrinkage in Bayesian Vector Autoregressive Models, Journal of Business & Economic Statistics. Vol. 29. No. 4, 1803-1829.
  • 23. Gao, Z. & Ling, S. (2018). Statistical inference for structurally changed threshold autoregressive models. Statistica Sinica. Vol. 29. No. 4, 1803-1829.
  • 24. Nyoni, T. & Nathaniel S. P. (2019). Modeling Rates of Inflation in Nigeria: An Application of ARMA, ARIMA and GARCH models. Munich Personal RePEc Archive. 91351.
  • 25. Zhang, X. & Xu, L. & Ding, F. & Hayat, T. (2018). Combined state and parameter estimation for a bilinear state space system with moving average noise. Journal of the Franklin Institute. Vol. 355. No. 6, 3079-3103.
  • 26. Jackson, E.A. (2018). Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index. Revista Economica. Vol. 70. No. 1, 53-65.
  • 27. Chen, G. & Gan, M. & Chen, G. (2018). Generalized exponential autoregressive models for nonlinear time series: Stationarity, estimation and applications. Information Sciences. Vol. 438, 46-57.
  • 28. Gobbi, F. & Mulinacci, S. State-Dependent Autoregressive Model for Nonlinear Time Series: Stationarity, Ergodicity and Estimation Methods. Available at: https://ssrn.com/abstract=3431614 (accessed on 14.07.2022).
  • 29. McDowall, D. & McCleary, R. & Bartos, B. J. (2019). Interrupted Time Series Analysis, 1st ed. Oxford: University Press.
  • 30. Paparoditis, E. & Politis, D. N. (2018). The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econometric Reviews. Vol. 37. No. 9, 955-973.
  • 31. Kokoszka, P. & Young, G. (2016). KPSS test for functional time series. Statistics Advanced Journal of Theoretical and Applied Sciences. Vol. 50. No. 5.
  • 32. Kozłowski, E. & Borucka, A.& Szymczak, T. & et al. (2021). Predicting the Fatigue Life of a Ball Joint. Transport and Telecommunication Journal. Vol. 22. No. 4, 453-460.
  • 33. Ministry of Infrastructure. Sustainable Transport Development Strategy until 2030. 2019.
  • 34. Statistical information brochure. Available at: https://www.ztm.waw.pl/statystyki/ (accessed on 14.07.2022).
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ba81a86-7625-414c-a2b8-e8c2ae6def46
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.