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Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

Author

Listed:
  • Vitalii Naumov

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31155 Krakow, Poland)

  • Andrzej Szarata

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31155 Krakow, Poland)

  • Hanna Vasiutina

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31155 Krakow, Poland)

Abstract

Simulation models of transport systems are a key tool for solving many problems in the field of management of these systems. The methodologies for creating such models use datasets on both transport infrastructure and demand for the delivery of goods or passenger transport, however, many factors are considered based on assumptions due to the complexity. This article describes the approach to modeling the cargo transportation system for road transport in Poland based on data obtained by the Central Statistical Office from the TD-E survey. This approach avoids many assumptions about demand as the demand parameters are estimated based on a sample representing the general population—a set of all economic entities generating freight traffic. Basic procedures in the developed approach have been implemented as Python scripts. As a result of the use of the proposed methodology, a country-wide road transport model was obtained based on the TD-E survey from 2018. The adequacy of the developed model was assessed based on the results of the General Traffic Measurement from 2015. The obtained model is of satisfactory quality (the coefficient of determination equals 0.62), which can be improved after calibrating the space resistance functions and improving the traffic distribution procedure.

Suggested Citation

  • Vitalii Naumov & Andrzej Szarata & Hanna Vasiutina, 2022. "Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland," Energies, MDPI, vol. 15(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5111-:d:861780
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    References listed on IDEAS

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