Russian Federation
In urban traffic flow prediction, the lack of analysis of the spatial correlation characteristics of traffic flow leads to a large deviation between the prediction results and the real traffic flow data. Therefore, the study takes into account the spatial correlation characteristics of traffic flow and introduces the graph convolution algorithm into the mathematical model, so that the coupled mathematical model can meet the requirements of time series processing of traffic flow data. Finally, the validity of the model was verified by testing the MAE and RMSE values of the model.
traffic flow prediction, mathematical modelling, ITS
1. Mehdi M.Z., Kammoun H.M., Benayed N.G., et al. Entropy-based traffic flow labeling for CNN-based traffic congestion prediction from meta-parameters [J]. IEEE Access, 2022, 10: 16123-16133.
2. Vijayalakshmi B, Ramar K, Jhanjhi N Z, et al. An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city [J]. International Journal of Communication Systems, 2021, 34(3): e4609.
3. Czyan, C. Metody kratkosrochnogo prognozirovaniya transportnyh potokov na osnove bol'shih dannyh / C. Czyan, A. A. Feofilova // Nauka i innovacii v sovremennom mire : Materialy Nacional'noy nauchno-prakticheskoy konferencii, Voronezh, 22 yanvarya 2024 goda. – Voronezh: Voronezhskiy gosudarstvennyy lesotehnicheskiy universitet im. G.F. Morozova, 2024. – S. 5-9. – DOIhttps://doi.org/10.58168/SIMW2024_5-9.
4. Czyan, C. Issledovanie metodov zapolneniya nedostayuschih dannyh pri prognozirovanii transportnyh potokov / C. Czyan // Aktual'nye problemy nauki i tehniki. 2024 : Materialy Vserossiyskoy (nacional'noy) nauchno-prakticheskoy konferencii, Rostov-na-Donu, 19–21 marta 2024 goda. – Rostov-na-Donu: Donskoy gosudarstvennyy tehnicheskiy universitet, 2024. – S. 682-684.
5. Feofilova, A. A. Upravlenie i kontrol' transportnyh potokov intellektual'noy sistemoy / A. A. Feofilova, C. Czyan // Molodoy issledovatel' Dona. – 2024. – T. 9, № 2(47). – S. 45-49.
6. UTD19 // URL: https://utd19.ethz.ch/index.html (data obrascheniya: 22.09.2024).
