Abstract
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(N^2) 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). PM-DMNet employs a novel dynamic memory network to capture traffic pattern features with only O(N) complexity, significantly reducing computational overhead while achieving excellent performance. 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 forecasting 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: this https URL.
Abstract (translated)
近年来，在交通预测领域，深度学习越来越受到关注。现有的交通预测模型通常依赖于 GCNs 或具有 O(N^2) 复杂性的注意力机制来动态提取交通节点特征，这些模型缺乏效率并且不是轻量级的。此外，这些模型通常仅利用历史数据进行预测，而没有考虑目标信息对预测的影响。为了解决这些问题，我们提出了一个模式匹配动态内存网络（PM-DMNet）。PM-DMNet 采用了一种新颖的动态内存网络来捕获交通模式特征，其复杂度仅为 O(N)，从而大大减少了计算开销，同时实现了卓越的性能。PM-DMNet 还引入了两种预测方法：递归多步预测（RMP）和并行多步预测（PMP），它们利用预测目标的时序特征来协助进行预测过程。此外，PMP 中还引入了转移注意力机制，将历史数据特征更好地对预测目标的状态进行对齐，从而更准确地捕捉趋势变化并减少错误。大量实验证明，与现有基准相比，所提出的模型具有优越性。源代码可在此处访问：https:// this URL。
URL
https://arxiv.org/abs/2408.07100
