GRAPH NEURAL NETWORKS FOR TRAFFIC FLOW PREDICTION: INNOVATIVE APPROACHES, PRACTICAL USAGE, AND SUPERIORITY IN SPATIO-TEMPORAL FORECASTING
- DOI
- 10.20998/2079-0023.2025.02.05
- Published
- 2025-12-29
- Container
- Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies
- Publisher
- National Technical University Kharkiv Polytechnic Institute
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.20998/2079-0023.2025.02.05,
title = {GRAPH NEURAL NETWORKS FOR TRAFFIC FLOW PREDICTION: INNOVATIVE APPROACHES, PRACTICAL USAGE, AND SUPERIORITY IN SPATIO-TEMPORAL FORECASTING},
author = {Bohdan Dokhniak and Viktor Khavalko},
year = {2025},
journal = {Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies},
doi = {10.20998/2079-0023.2025.02.05},
url = {https://doi.org/10.20998/2079-0023.2025.02.05}
}RIS
TY - JOUR TI - GRAPH NEURAL NETWORKS FOR TRAFFIC FLOW PREDICTION: INNOVATIVE APPROACHES, PRACTICAL USAGE, AND SUPERIORITY IN SPATIO-TEMPORAL FORECASTING AU - Bohdan Dokhniak AU - Viktor Khavalko PY - 2025 JO - Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies DO - 10.20998/2079-0023.2025.02.05 UR - https://doi.org/10.20998/2079-0023.2025.02.05 ER -
APA
Dokhniak, B., & Khavalko, V. (2025). GRAPH NEURAL NETWORKS FOR TRAFFIC FLOW PREDICTION: INNOVATIVE APPROACHES, PRACTICAL USAGE, AND SUPERIORITY IN SPATIO-TEMPORAL FORECASTING. Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies. https://doi.org/10.20998/2079-0023.2025.02.05
Source records
- crossref · retrieved 2026-09-25T15:27:52.520Z