Related Experiment Video
Updated: Jul 9, 2026
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
575
RGDAN: A random graph diffusion attention network for traffic prediction
Jin Fan1, Wenchao Weng2, Hao Tian3
1Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, Hangzhou, China.
Summary
This study introduces a Random Graph Diffusion Attention Network (RGDAN) for improved traffic prediction. RGDAN enhances spatial and temporal feature extraction, leading to more accurate traffic flow forecasts.
Area of Science:
- Artificial Intelligence
- Transportation Engineering
- Network Science
Background:
- Traffic prediction relies on graph structures, but road networks are complex with variable temporal features.
- Current methods use fixed weights (e.g., distance) and overlook road characteristics and traffic flow correlations.
- Existing models often ignore global spatial dependencies and struggle with information extraction at limited graph depths.
Purpose of the Study:
- To develop an advanced model for accurate traffic prediction.
- To address limitations in spatial feature extraction and temporal dependency modeling.
- To improve the precision of traffic flow forecasts in complex road networks.
Main Methods:
- Proposed a novel Random Graph Diffusion Attention Network (RGDAN).
- RGDAN integrates a graph diffusion attention module for adaptive spatial weight learning.
- Incorporated a temporal attention module to capture temporal correlations.
Main Results:
- RGDAN demonstrated superior performance on three large-scale public datasets.
- Achieved 2%-5% higher prediction precision compared to state-of-the-art methods.
- Effectively captured both local and global spatial dependencies and temporal correlations.
Conclusions:
- RGDAN offers a significant advancement in traffic prediction accuracy.
- The model's adaptive weighting and attention mechanisms enhance spatial and temporal feature extraction.
- RGDAN provides a more robust solution for complex transportation network analysis.
