Szczegóły publikacji

Opis bibliograficzny

Sample and Aggregate Voronoi Neighborhood Weighted Graph Neural Network (SAGE-Voronoi) and its capability for city-sized vehicle traffic time series prediction / Przemysław BIELECKI, Tomasz HACHAJ, Jarosław WĄS // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417 . — 2025 — vol. 15 iss. 24 art. no. 12899, s. 1–18. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16–18, Abstr. — Publikacja dostępna online od: 2025-12-07

Autorzy (3)

Słowa kluczowe

short term city sized data graph neural network Voronoi Neighborhood traffic prediction sensor data long short-term memory convolutional network

Dane bibliometryczne

ID BaDAP	165085
Data dodania do BaDAP	2026-01-08
Tekst źródłowy	URL
DOI	10.3390/app152412899
Rok publikacji	2025
Typ publikacji	artykuł w czasopiśmie
Otwarty dostęp
Creative Commons
Czasopismo/seria	Applied Sciences (Basel)

Abstract

The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region. This paper fills this gap by proposing and evaluating the Sample and Aggregate-Voronoi method (SAGE-Voronoi), which utilizes the novel concept of Voronoi Neighborhood Weighted Graph-based convolutional networks to predict car traffic in cities. It demonstrates the usefulness of this method for short-term predictions using real sensor data from the moderate-sized town of Darmstadt. The results obtained are compared with those of other neural network algorithms, namely pure Long Short-Term Memory, SAGE, Diffusion Convolutional Gated Recurrent Unit (DCGRU), and Spatio-Temporal Graph Convolutional Neural Network (STGCN). SAGE-Voronoi obtained significantly better results than the state-of-the-art approaches. The SAGE-Voronoi graph neural network enables the reliable prediction of varying car traffic among network nodes. The proposed approach is not limited to spatiotemporal traffic data and can be utilized in other similar domains. The source code and dataset used in our experiments are available for download, enabling full reproducibility of the results.