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

Abstract 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.