Graph Neural Network for Traffic Forecasting: A Survey
Jan 27, 2021 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic …
Traffic prediction with advanced Graph Neural Networks
Sep 3, 2020 · In a Graph Neural Network, a message passing algorithm is executed where the messages and their effect on edge and node states are learned by neural networks. From this …
Traffic forecasting using graph neural networks and LSTM - Keras
Dec 28, 2021 · This example shows how to forecast traffic condition using graph neural networks and LSTM. Specifically, we are interested in predicting the future values of the traffic speed …
Adaptive traffic prediction model using Graph Neural Networks …
Graph Neural Networks (GNNs) have emerged as a promising solution for traffic prediction by modeling traffic networks as graphs. GNNs effectively capture spatial and temporal …
Graph Neural Network for Traffic Forecasting: The Research …
Feb 27, 2023 · Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. …
Graph Neural Networks Empowered Origin‐Destination Learning …
May 29, 2025 · In this paper, we proposed STOD-Net, a spatial-temporal origin-destination feature-enhanced deep neural network, to solve urban traffic prediction. Beyond modelling the …
Traffic Prediction Using Graph Neural Network - IEEE Xplore
Jun 25, 2023 · In recent years, graph neural networks (GNNs) have shown great potential in predicting traffic by taking into account the graph structure of road networks. In this project, we …
Emerging Trends in Graph Neural Networks for Traffic Flow Prediction…
Apr 11, 2025 · Graph Neural Networks (GNNs) have emerged as a powerful tool for traffic flow prediction, demonstrating significant advancements in modelling complex spatial-temporal …
Traffic Prediction with Graph Neural Network: A Survey
Dec 14, 2021 · Graph data structure can well express the topology structure of traffic network, so graph model has more development space in the field of traffic prediction. The main purpose …
GitHub - jwwthu/GNN4Traffic: This is the repository for the …
Description: A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning. Based on simulation results of …
Traffic flow matrix-based graph neural network with attention …
Apr 1, 2024 · Traffic flow matrix can bring improvements to the graph neural network for traffic forecasting. The designed graph neural network can predict the traffic flow more accurately. …
In this survey, we review the rapidly growing body of research using di erent graph neural networks, e.g. graph convolutional and graph attention networks, in various tra c forecasting …
Graph convolutional neural networks for traffic forecasting and ...
May 28, 2024 · Within this paper, an extensive exploration unfolds, delving into the utilization of GCNs in the realm of traffic forecasting and prediction. It meticulously illuminates their inherent …
Traffic flow prediction based on spatiotemporal encoder …
May 30, 2025 · To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction …
Transfer Learning in Traffic Prediction with Graph Neural Networks
Sep 22, 2021 · Statistics on urban traffic speed flows are essential for thoughtful city planning. Recently, data-driven traffic prediction methods have become the state-of-th.
ST-GTrans: Spatio-temporal graph transformer with road network …
Accurate traffic prediction has significant implications for traffic optimization and management. However, few studies have thoroughly considered the implicit spatial semantic information and …
Traffic Prediction Based on Formal Concept-Enhanced Federated Graph …
Jan 29, 2025 · Aiming to improve the efficiency of urban traffic management, previous studies have achieved considerable traffic prediction accuracy. For example, methods based on time …
Complex Network Traffic Prediction Method Under Graph Neural Network
Nov 6, 2024 · Accurate and real-time network traffic prediction has an important role in networks, and it is also essential in traffic engineering and network control. The current prediction …
Graph neural network for traffic forecasting: A survey
Nov 30, 2022 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic …
Accurate V2X traffic prediction with deep learning architectures
A comparative study of existing network traffic prediction methods, as presented in Table 1, further underscores these challenges. ... Zhao Z., Chang Z., Wang Z. (2022). Spatial-temporal …