2024/08/29 by Osama Ahmad, Ahmad, Osama, Zubair Khalid +3 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2408.16191
openalex publication_date 2024/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper focuses on spatiotemporal (ST) traffic prediction using graph neural networks. Given that ST data consists of non-stationary and complex time events, interpreting and predicting such trends is comparatively complicated. Representation of ST data in modes helps us to infer behavior and assess the impact of noise on prediction applications. We propose a framework that decomposes ST data into modes using the variational mode decomposition (VMD) method, which is then fed into the neural network for forecasting future states. This hybrid approach is known as a variational mode graph convolutional network (VMGCN). Instead of exhaustively searching for the number of modes, they are determined using the reconstruction loss from the real-time application data. We also study the significance of each mode and the impact of bandwidth constraints on different horizon predictions in traffic flow data. We evaluate the performance of our proposed network on the LargeST dataset for both short and long-term predictions. Our framework yields better results compared to state-of-the-art methods.