Multi-scale feature enhanced spatio-temporal learning for traffic flow forecasting

Abstract

Traffic flow forecasting is a critical task within Intelligent Transportation Systems (ITS). Its main challenge lies in effectively modeling the complex traffic related big data, including intricate intra-channel and inter-channel correlations, as well as dynamic spatio-temporal dependencies. Furthermore, current methods continue to encounter bottlenecks in extracting and learning complex and dynamic spatio-temporal features from the original traffic data for long-term prediction, resulting in challenges related to model robustness and generalization. In response to these, we introduce a novel deep learning model with multi-scale feature enhancement for traffic flow forecasting, which is based on the attention mechanism and the graph convolution learning framework. We first introduce the integration design of the spatio-temporal dependency features enhancement module with the base attention learning block through a memory embedding layer. Then we propose a traffic network topology features enhancement module with the spatial attention layer, enabling dynamic enhanced learning of spatio-temporal dependency features. This comprehensive approach enables the model to effectively learn complex and dynamic spatio-temporal dependencies, capturing key patterns in traffic flow data. Through extensive experimental evaluations using traffic flow forecasting benchmarks, we have validated the superior performance of the proposed model over the state-of-the-art.