Aug 10, 2022 · In this study, we present a self-attention based multi-module fusion graph convolution network for the traffic flow prediction (SAMGCN) method ...

We present a traffic flow prediction framework called MIFGCN. This framework utilizes attention-based interactive learning to extract multiscale temporal ...

Sep 6, 2023 · The traffic flow prediction model analyzes large amounts of historical traffic data to estimate future traffic condi- tions based on statistical ...

Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction. https://doi.org/10.1007/978-981-19-5194-7_1.

Oct 1, 2023 · We propose a multi-Graph Fusion-based Graph Convolutional Network (GFGCN) for traffic prediction, where a multi-graph fused graph convolutional module is ...

May 22, 2024 · Precise and reliable traffic predictions play a vital role in contemporary traffic management, particularly within complex traffic networks.

Dec 9, 2023 · In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC).

Mar 7, 2023 · In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal ...

Jun 20, 2024 · We propose the MFSTN, a multi-feature spatial–temporal fusion network, which include the temporal transformer encoder and graph attention network.

In this paper, we propose a novel dynamic graph convolution network with attention fusion to tackle this gap. The method first enhances the interaction of ...