CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction

TL;DR

CoMemNet is a continual learning framework for traffic prediction that uses contrastive sampling and memory replay to adapt to evolving traffic network patterns, achieving state-of-the-art results.

Contribution

It introduces a dual-branch continual learning model with dynamic contrastive sampling and a lightweight memory buffer for traffic prediction in evolving networks.

Findings

Achieves state-of-the-art performance on three large-scale datasets.

Effectively mitigates catastrophic forgetting in streaming traffic data.

Utilizes Wasserstein Distance features for dynamic node sampling.

Abstract

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.