The application of neural networks for the life-cycle analysis of road and rail rolling stock during the operational phase

• Autorzy: Regulski Paweł , Abramek Karol F.

Identyfikatory

DOI

10.37705/TechTrans/e2022002

• Języki publikacji: EN

Abstrakty

EN

The aim of this article is to assess the possibility of using neural networks to analyse the life cycle of rolling stock in the operational phase by selecting the number of rolling stock sets and rail using the example of public transport in the Szczecin agglomeration. The research was conducted in September 2019 and June 2020. It included the number of tram and bus rolling stock sets on individual public transport lines based on data from the Central Public Transport Management System in the Szczecin agglomeration. The research, which was based on comparative analyses of individual types of rolling stock and their technical and economic data, took into account the life-cycle assessment criteria associated with the operation of vehicles in relation to the number of rolling stock sets. The use of neural networks on the example of the city of Szczecin for the purpose of life-cycle analysis, can make a significant contribution to creating a decision model for the improvement of public transport in cities with various types of public transport vehicles.

Słowa kluczowe

EN

neural networks; life cycle; road rolling stock; rail rolling stock; operation

PL

sieci neuronowe; koło życia; tabor drogowy; tabor kolejowy; operacja

• Wydawca: Wydawnictwo Politechniki Krakowskiej im. Tadeusza Kościuszki
• Czasopismo: Technical Transactions
• Rocznik: 2022
• Tom: Vol. 119, iss. 1
• Strony: art. no. e2022002
• Opis fizyczny: Bibliogr. 25 poz., tab., wykr.

Twórcy

autor

Regulski Paweł

• Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in Szczecin

• https://orcid.org/0000-0002-5195-0335

autor

Abramek Karol F.

• Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in Szczecin

• https://orcid.org/0000-0001-9704-5253

Bibliografia

• 1. Arena, M., Azzone, G., Conte, A. (2013). A streamlined LCA framework to support early decision making in vehicle development. Journal of Cleaner Production, 41(2), 105–113. https://doi.org/10.1016/j.jclepro.2012.09.031
• 2. Basu, A., Raja, B., Gracious, R., Vanajakshi, L. (2020). Dynamic trip planner for public transport using genetic algorithm. Transport, 35(2), 156–167. https://doi.org/10.3846/transport.2020.12477
• 3. Birr, K. (2018). Mode choice modelling for urban areas. Technical Transactions, 6, 67–78. http://dx.doi.org/10.4467/2353737XCT.18.087.8692
• 4. Buehler, R., Pucher, J. (2011). Making public transport financially sustainable. Transport Policy, 18(1), 126–138. https://doi.org/10.1016/j.tranpol.2010.07.002
• 5. Danilecki, K., Mrozik, M., Smurawski, P. (2017). Changes in the environmental profile of a popular passenger car over the last 30 years – Results of a simplified LCA study. Journal of Cleaner Production, 141(1), 208–218. https://doi.org/10.1016/j.jclepro.2016.09.050
• 6. Del Pero, F., Delogu, M., Pierini, M. (2017). The effect of lightweighting in automotive LCA perspective: Estimation of mass-induced fuel consumption reduction for gasoline turbocharged vehicles. Journal of Cleaner Production, 154(6), 566–577. https://doi.org/10.1016/j.jclepro.2017.04.013
• 7. Dudek, M., Richter, M., Solecka, K. (2018). A multi-criteria appraisal of the selection of means of urban passenger transport using the electre and ahp methods. Technical Transactions, 6, 79–94. http://dx.doi.org/10.4467/2353737XCT.18.088.8693
• 8. Holmgren, J. (2007). Meta-analysis of public transport demand. Transportation Research Part A: Policy and Practice, 41(12), 1021–1035. https://doi.org/10.1016/j.tra.2007.06.003
• 9. Kiciński, M., Solecka, K. (2018). Application of MCDA/MCDM methods for an integrated urban public transportation system – case study, city of Cracow. Archives of Transport, 46(2), 71–84. https://doi.org/10.5604/01.3001.0012.2107
• 10. Konowrocki, R., Chojnacki, A. (2020). Analysis of rail vehicles’ operational reliability in the aspect of safety against derailment based on various methods of determining the assessment criterion. Eksploatacja i Niezawodność – Maintenance and reliability, 22(1), 73–85. http://dx.doi.org/10.17531/ein.2020.1.9
• 11. Mrozik, M., Eliasz, J., Terelak-Tymczyna, A. (2013). Material-energy model of motor vehicle life cycle. Strojarstvo, 55(2), 161–168.
• 12. Niewczas, A., Rymarz, J., Debicka, E. (2019). Stages of operating vehicles with respect to operational efficiency using city buses as an example. Eksploatacja i Niezawodność – Maintenance and Reliability, 21(1), 21–27. http://dx.doi.org/10.17531/ein.2019.1.3
• 13. Paulley, N., Balcombe, R., Mackett, R., Titheridge, H., Preston, J., Wardman, M., Shires, J., White, P. (2006). The demand for public transport: The effects of fares, quality of service, income and car ownership. Transport Policy, 13(7), 296–305. https://doi.org/10.1016/j.tranpol.2005.12.004
• 14. Pravilonis, T., Sokolovskij, E. (2020). Analysis of composite material properties and their possibilities to use them in bus frame construction. Transport, 35(4), 368–378. https://doi.org/10.3846/transport.2020.13018
• 15. Pucher, J., Buehler, R. (2009). Integrating Bicycling and Public Transport in North America. Journal of Public Transportation, 12(3), 79–104. http://doi.org/10.5038/2375-0901.12.3.5
• 16. Raugei, M., Winfield, P. (2019). Prospective LCA of the production and EoL recycling of a novel type of Li-ion battery for electric vehicles. Journal of Cleaner Production, 213(3), 926–932. https://doi.org/10.1016/j.jclepro.2018.12.237
• 17. Selech, J., Andrzejczak, K. (2020). An aggregate criterion for selecting a distribution for times to failure of components of rail vehicles. Eksploatacja i Niezawodność – Maintenance and reliability, 22(1), 102–111. http://dx.doi.org/10.17531/ein.2020.1.12
• 18. Świderski, A., Jóźwiak, A., Jachimowski, R. (2018). Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja i Niezawodność – Maintenance and Reliability, 20(2), 292–299. http://dx.doi.org/10.17531/ein.2018.2.16
• 19. Thompson, K., Schofield, P. (2007). An investigation of the relationship between public transport performance and destination satisfaction. Journal of transport geography, 15(3), 136–144. https://doi.org/10.1016/j.jtrangeo.2006.11.004
• 20. http://www.infotram.pl (access: 9.07.2020).
• 21. http://www.pesa.pl (access: 19.10.2020).
• 22. http://www.pim.pl (access: 9.07.2020).
• 23. http://www.solarisbus.com (access: 19.10.2020).
• 24. http://www.spak.pl (access: 3.07.2020).
• 25. http://www.ts.szczecin.pl (access: 3.07.2020).

Uwagi

Section "Mechanics"