Strategic Forecasting of Multimodal Container Traffic Basing on Transport and Economic Balance of the Russian Federation

Autor: Dmitry Kagan, Oleg Evseev, Elena Anikina, Alexander Shubin, Andrey Kryazhev, Kirill Tulenev, Anton Shubin, Anton Urazov, Alexander Zaboev, Vasily Murashov, Victor Buslov, Anton Zemtsov
Rok vydání: 2021
Předmět:
Zdroj: Journal of Physics: Conference Series. 1828:012134
ISSN: 1742-6596
1742-6588
DOI: 10.1088/1742-6596/1828/1/012134
Popis: International cargo containerization system continues progressive development supporting increase of multimodal transport traffic. Containerized commodity transportation schemes are highly efficient for majority of transcontinental and long-distance deliveries optimizing costs, time and quality of transport operations basing on exact forecasting of container turnover. Following these headline routes, research article represents actual methodology of forecasting cargo volumes in accordance with Big Data stated in Transport and Economic Balance of the Russian Federation (TEB), as per spatial input-output predictions of freight traffic between regions of the country by rail, road, inland water and maritime transport by types of commodities. Expanding transportation network is linked to the core freight multimodal transport and logistics centers (TLC), connected with 12 transport hubs having strategic value for Russian economics. Represented research algorithms consider cargo base for 12 TLCs in backbone network subject to types of commodities, growth production and consumption, import and export balance in the strategic timelines of 2024 and 2035. Methodology of forecasting container traffic balance across the country is based on coefficients of container demand for each category of cargo as well as transport modes and transportation schemes. Container traffic forecast indicated by scenarios of TEB model reflect strategy of development and optimization for the freight flows in TLC network. These information models, due to their complex structure and rich semantics, are more likely to belong to the class of models based on knowledge, than on data, that requires further improvement of forecasting methods using intelligent processing of Big Knowledge-Based models.
Databáze: OpenAIRE
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