CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
This work addresses the problem of catastrophic forgetting in continual traffic prediction for streaming networks, which is a practical challenge for real-time traffic management systems.
CoMemNet proposes a dual-branch continual learning framework for traffic prediction that uses a Dynamic Contrastive Sampler and a Node-Adaptive Temporal Memory Buffer to handle evolving streaming traffic networks, achieving state-of-the-art performance on three large-scale real-world datasets.
In recent years, the integration of non-topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio-temporal information in non-Euclidean graphs. However, most existing methods rely on static underlying graph structures, which are inadequate for capturing the continuously expanding and evolving patterns in streaming traffic networks. To address this challenge, we propose a simple yet efficient dual-branch continual learning framework for traffic prediction, named CoMemNet. The fast-converging Online branch undertakes the primary prediction tasks, while the momentum-updated Target branch extracts historical information using Wasserstein Distance features to create a Dynamic Contrastive Sampler (DC Sampler). This sampler selects a node set with significant dynamic network feature changes for training, effectively mitigating the issue of catastrophic forgetting. Additionally, the backbone incorporates a lightweight Node-Adaptive Temporal Memory Buffer (TMRB-N) to consolidate old knowledge through memory replay and address the risk of memory explosion. Finally, we provide two newly curated open-source datasets. Experimental results demonstrate that CoMemNet achieves state-of-the-art (SOTA) performance across all three large-scale real-world datasets. The code is available at: https://github.com/meiwu5/CoMemNet.