Examining the possibility of short-term prediction of traffic volume in smart city control systems with the use of regression models

• Autorzy: Dymora Paweł , Mazurek Mirosław , Jucha Maksymilian

Identyfikatory

DOI

10.24425/ijet.2023.147711

• Języki publikacji: EN

Abstrakty

EN

This article deals with issues related to the optimization of traffic management in modern cities, the so-called Smart City. In particular, the article presents the process of evolution of the traffic flow prediction model at a selected crossroads in a selected city in Poland - the city of Rzeszów. Rzeszow is an example of a smart city equipped with an extensive system of real-time data collection and processing from multiple road points in the city. The research was aimed at a detailed analysis of the feasibility and degree of fit of different variants of the regression model: linear, polynomial, trigonometric, polynomial-trigonometric, and regression-based Random Forest algorithm. Several studies were carried out evaluating different generations of models, in particular, an analysis was carried out based on which the superiority of the trigonometric model was demonstrated. This model had the best fit and the lowest error rate, which could be a good conclusion for widespread use and implementation in Smart City supervisory systems.

Słowa kluczowe

EN

Smart City; traffic flow; prediction; regression; polynomial-trigonometric model; trigonometric model; RandomForest model

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 1
• Strony: 31--38
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Dymora Paweł

• Rzeszów University of Technology, Faculty of Electrical and Computer Engineering

autor

Mazurek Mirosław

• Rzeszów University of Technology, Faculty of Electrical and Computer Engineering

autor

Jucha Maksymilian

• Rzeszów University of Technology, Faculty of Mathematics and Applied Physics

Bibliografia

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• [13] P. Dymora, M. Mazurek, Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks. Appl. Sci. 2021, 11, 190. doi:10.3390/app11010190
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).