Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms with O(N²) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). Unlike traditional attention and graph convolution-based approaches, PM-DMNet employs a novel dynamic memory network that stores the most representative traffic patterns from historical data in a memory matrix through training. It captures traffic pattern features by comparing the similarity between the memory matrix and the current traffic state. This method not only achieves excellent predictive performance but also significantly reduces computational complexity to O(N). The PM-DMNet also introduces two prediction methods: Recursive Multi-step Prediction (RMP) and Parallel Multi-step Prediction (PMP), which leverage the time features of the prediction targets to assist in the prediction process. Furthermore, a transfer attention mechanism is integrated into PMP, transforming historical data features to better align with the predicted target states, thereby capturing trend changes more accurately and reducing errors. Extensive experiments demonstrate the superiority of the proposed model over existing benchmarks. The source codes are available at: https://github.com/wengwenchao123/PM-DMNet