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A hybrid model for forecasting the volume of passenger flows on Serbian railways

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Abstract

The accuracy of predicting the volume of railway passenger flows is very significant because of the vital role in the basic functions of transportation resources management. Although dealing with this problem is very often based on the use of the neural networks, the uncertainty which dominates in the functioning of transportation systems is of great significance. The neural networks have been used for the time-series prediction with good results. This research compared two methods the parametric and the non-parametric approach. This study aims at presenting a hybrid model based on the integration of the genetic algorithm (GA) and the artificial neural networks (ANN) for forecasting the monthly volume of passengers on the Serbian railways. This innovative hybrid demonstrates how the genetic algorithms can be used to optimize the network architecture. By applying the idea of genetic algorithms in the neural networks, the integration is used so that on the basis of the input data, the selected population represents the number of neurons in the middle. In order to assess performances, the developed approach is compared to the traditional SARIMA model and the proposed method GAANN is better.

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Acknowledgments

The work presented here was supported by the Serbian Ministry of Education and Science (Project III44006 and I 36022) and project ROUTER: Development of Research Teams at the University of Pardubice CZ.1.07./2.3.00/30.0058.

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Authors and Affiliations

  1. Department for Mathematical Sciences, State University of Novi Pazar, Vuka Karadžića bb, Novi Pazar, 36300, Serbia

    Nataša Glišović

  2. Division for Management in Railway, Rolling Stock and Traction, The Faculty of Traffic and Transport Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11000, Serbia

    Miloš Milenković, Nebojša Bojović & Zoran Avramović

  3. Department of Transport Management, Marketing and Logistics, Jan Perner Transport Faculty, University of Pardubice, Studentska 95, 53210, Pardubice, Czech Republic

    Libor Švadlenka

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  1. Nataša Glišović
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  2. Miloš Milenković
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  3. Nebojša Bojović
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  4. Libor Švadlenka
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  5. Zoran Avramović
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Correspondence to Nataša Glišović.

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Glišović, N., Milenković, M., Bojović, N. et al. A hybrid model for forecasting the volume of passenger flows on Serbian railways. Oper Res Int J 16, 271–285 (2016). https://doi.org/10.1007/s12351-015-0198-5

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