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

Author

Listed:
  • Nataša Glišović

    (State University of Novi Pazar)

  • Miloš Milenković

    (University of Belgrade)

  • Nebojša Bojović

    (University of Belgrade)

  • Libor Švadlenka

    (University of Pardubice)

  • Zoran Avramović

    (University of Belgrade)

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.

Suggested Citation

  • Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    • Handle: RePEc:spr:operea:v:16:y:2016:i:2:d:10.1007_s12351-015-0198-5
    DOI: 10.1007/s12351-015-0198-5
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    References listed on IDEAS

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    Cited by:

    1. Yulong Pei & Songmin Ran & Wanjiao Wang & Chuntong Dong, 2023. "Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    2. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.

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