CoMemNet：基于记忆回放网络的对比采样用于持续交通预测
CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction
AI总结：
CoMemNet提出一种双分支持续学习框架，通过动态对比采样和轻量级记忆缓冲器解决持续交通预测中的灾难性遗忘问题，实验证明其在三大真实世界数据集上达到SOTA性能。
摘要：
CoMemNet提出了一种双分支持续学习框架，通过动态对比采样和轻量级记忆缓冲器解决持续交通预测中的灾难性遗忘问题，实验证明其在三大真实世界数据集上达到SOTA性能。
英文摘要：
Traffic sensor networks evolve as sensors are added and traffic distributions change, whereas most forecasting models assume a fixed node set and repeatedly retrain on all available data. We propose CoMemNet, a Continual Memory Network for efficient prediction over evolving traffic sensor networks. CoMemNet uses an Online branch to adapt to the current period and an exponential-moving-average Target branch as a stable feature reference. A Wasserstein-based Drift Sampler compares node-wise Online-Target feature distributions and selects a limited set of drift-sensitive nodes for updating. A lightweight Node-Adaptive Temporal Memory Replay Buffer (TMRB-N) retains compact temporal states without repeatedly traversing all historical training data. The prediction backbone does not consume an adjacency matrix; sensor adjacency is used only to construct data and optionally expand the selected update set to a limited neighborhood. Experiments on three multi-period PeMS datasets include three-seed evaluation, strong static retraining and continual baselines, controlled sampling strategies, continual-learning metrics, robustness tests, and resource accounting. The results show that CoMemNet maintains stable prediction accuracy and efficient adaptation under bounded shared-node selection, achieving a better balance between historical knowledge preservation and current-period prediction performance. Meanwhile, as the evolving network expands, CoMemNet shows clearer accuracy and cumulative training-time advantages over current-period retraining baselines. The code is available at:https://meiwu5.github.io/CoMemNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
