Forecasting the estimated time of arrival for a cargo dispatch delivered by a freight train along a railway section

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.170174

Keywords:

railroad network, expected time of arrival, artificial neural network

Abstract

This paper reports a method for predicting the expected time of arrival (ETA) of a cargo dispatch taking into consideration determining the duration at which a freight train travels along a railroad section where trains move not complying with a departure schedule. A characteristic feature of railroads with such a traffic system is the difficulty in predicting the stages of a transportation process, which necessitates the development of effective methods of forecasting. Based on correlation analysis, we have determined the dependence of the general macro-characteristics of train flow and individual parameters of a freight train on the duration of its movement along a section. It has been proposed to represent the dependence of predicted duration of train movement along a railroad section on the following factors: traffic intensity and density along a section, the proportion of passenger trains in total train flows, the length of a train and its gross weight. All experimental studies are based on actual data on the operation of the distance Osnova-Lyubotyn at the railroad network AO Ukrzaliznytsya.

Based on a comparative analysis, using the indicators for accuracy and adequacy of several regression methods to predict ETA of cargo dispatch, we have chosen the regression model based on an artificial neural network MLP. To derive the MLP structure, a cross-validation method has been applied, which implies the validation of a mathematical model reliability based on the criteria of accuracy MAE and adequacy ‒ F-test. The structure of MLP has been obtained, which consists of five hidden layers. We predicted the time that it would take for a train to travel in facing direction along the Osnova-Lyubotyn section. For a given projection, the value for MAE was 0.0845, which is a rather high accuracy for this type of problems, and confirms the effectiveness of MLP application to solve the task on predicting a cargo dispatch ETA.

The current study provides a possibility to design in the future an automated system for predicting a cargo dispatch ETA for a mixed-traffic railroad system in which freight trains depart not complying with a regulatory schedule.

Author Biographies

Andrii Prokhorchenko, Ukrainian State University of Railway Transport Feuerbach sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Associate Professor

Department of Management of Operational Work

Larysa Parkhomenko, Ukrainian State University of Railway Transport Feuerbach sq., 7, Kharkiv, Ukraine, 61050

PhD, Associate Professor

Department of Management of Operational Work

Halina Nesterenko, Dnipro National University of Railway Transport named after academician V. Lazaryan Lazariana str., 2, Dnipro, Ukraine, 49010

PhD, Associate Professor

Department of Management of Operational Work

Mykhailo Muzykin, Dnipro National University of Railway Transport named after academician V. Lazaryan Lazariana str., 2, Dnipro, Ukraine, 49010

PhD, Senior Lecturer

Department of Life Activity Safety

Halyna Prokhorchenko, Ukrainian State University of Railway Transport Feuerbach sq., 7, Kharkiv, Ukraine, 61050

Аssistant

Department of Management of Operational Work

Alina Kolisnyk, Ukrainian State University of Railway Transport Feuerbach sq., 7, Kharkiv, Ukraine, 61050

Аssistant, Postgraduate student

 Department of Cargo and Commercial Management

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Published

2019-06-12

How to Cite

Prokhorchenko, A., Panchenko, A., Parkhomenko, L., Nesterenko, H., Muzykin, M., Prokhorchenko, H., & Kolisnyk, A. (2019). Forecasting the estimated time of arrival for a cargo dispatch delivered by a freight train along a railway section. Eastern-European Journal of Enterprise Technologies, 3(3 (99), 30–38. https://doi.org/10.15587/1729-4061.2019.170174

Issue

Vol. 3 No. 3 (99) (2019): Control processes

Section

Control processes

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Copyright (c) 2019 Andrii Prokhorchenko, Artem Panchenko, Larysa Parkhomenko, Halina Nesterenko, Mykhailo Muzykin, Halyna Prokhorchenko, Alina Kolisnyk

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