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- Title
Adaptive Correlation Graph Neural Ordinary Differential Equation for Traffic Flow Forecasting.
- Authors
Lin Bai; Zheng Huang; Shuang Wang; Hong Dai
- Abstract
Contemporary advancements in deep learning have spurred widespread adoption of spatio-temporal prediction across various scientific disciplines. Nonetheless, traffic flow prediction, as a quintessential spatio-temporal task, continues to present significant challenges, such as the accurate modeling of complex dependencies and dynamic changes over time and space. To address these issues, this paper introduces the Dual-Branch and Multi-Temporal Resolution Convolutional Network with an Adaptive Graph Neural Ordinary Differential Equation (DM-AGODE) model. This innovative approach integrates an optimized graph neural ordinary differential equation with an adaptive correlation adjacency graph, ensuring precise feature propagation across the network. The model incorporates a Dual-Branch Learning (DBL) mechanism to effectively differentiate between short-term dynamics and long-term trends, while the Multi-Temporal Resolution Convolution (MTRC) method enhances the processing of temporal data across multiple scales, critical for capturing the complex behaviors of traffic flow. Furthermore, to demonstrate the effectiveness of our model, we conducted a comprehensive evaluation of our model using six widely recognized real-world datasets, which highlighted its superior adaptability to complex traffic flow patterns. Compared to the leading baseline model, our approach achieves an improvement in prediction accuracy exceeding 8% and significantly enhances efficiency in processing.
- Subjects
TRAFFIC patterns; TRAFFIC flow; ORDINARY differential equations; TRAFFIC estimation; DEEP learning
- Publication
Engineering Letters, 2024, Vol 32, Issue 9, p1770
- ISSN
1816-093X
- Publication type
Article