Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/131115
Title: Modeling the dynamics of passenger traffic in road transport for the regional road network
Authors: Ie, O.
Issue Date: 2023
Publisher: American Institute of Physics Inc.
Citation: Ie, O 2023, Modeling the dynamics of passenger traffic in road transport for the regional road network: book chapter. в AG Galkin & SV Bushuev (ред.), 2021 International Scientific and Practical Conference on Railway Transport and Technologies, RTT 2021: book. Том. 2624, AIP Conference Proceedings, № 1, Том. 2624, American Institute of Physics Inc. https://doi.org/10.1063/5.0132237
Ie, O. (2023). Modeling the dynamics of passenger traffic in road transport for the regional road network: book chapter. в A. G. Galkin, & S. V. Bushuev (Ред.), 2021 International Scientific and Practical Conference on Railway Transport and Technologies, RTT 2021: book (Том 2624). (AIP Conference Proceedings; Том 2624, № 1). American Institute of Physics Inc.. https://doi.org/10.1063/5.0132237
Abstract: Problems of modeling seasonality in the study of the temporal distribution of passenger traffic in road transport according to the daily data on the example of the Sverdlovsk region for the period February-November 2021 are considered in the article. The use of a statistical approach based on adaptive models made it possible to obtain an adequate model with good statistical and predictive properties. Algorithms for constructing the Holt-Winters model and its software implementation in Python are considered. The indicators studied in the work had a number of features that were taken into account in the modeling. Point and interval estimates of the forecast of passenger traffic for December 2021 were found. © 2023 Author(s).
URI: http://elar.urfu.ru/handle/10995/131115
Access: info:eu-repo/semantics/openAccess
Conference name: 2021 International Scientific and Practical Conference on Railway Transport and Technologies, RTT 2021
Conference date: 24 November 2021 through 25 November 2021
SCOPUS ID: 85182007844
PURE ID: 51615604
ISSN: 0094-243X
ISBN: 9780735447066
DOI: 10.1063/5.0132237
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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