Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
This work improves traffic flow forecasting for intelligent transportation systems and smart cities, but it is incremental as it builds on existing graph-based methods with enhancements.
The paper tackles the problem of dynamic traffic flow forecasting by addressing the inability of existing methods to encode global temporal-spatial patterns and their tendency to overfit on pre-defined geographical correlations, resulting in a model that consistently outperforms strong baselines on various real-world traffic networks.
Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly nonlinear problem with complex temporal-spatial dependencies. Although the existing methods has provided great contributions to mine the temporal-spatial patterns in the complex traffic networks, they fail to encode the globally temporal-spatial patterns and are prone to overfit on the pre-defined geographical correlations, and thus hinder the model's robustness on the complex traffic environment. To tackle this issue, in this work, we proposed a multi-grained temporal-spatial graph learning framework to adaptively augment the globally temporal-spatial patterns obtained from a crafted graph transformer encoder with the local patterns from the graph convolution by a crafted gated fusion unit with residual connection techniques. Under these circumstances, our proposed model can mine the hidden global temporal-spatial relations between each monitor stations and balance the relative importance of local and global temporal-spatial patterns. Experiment results demonstrate the strong representation capability of our proposed method and our model consistently outperforms other strong baselines on various real-world traffic networks.