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

TL;DR

This paper introduces PM-DMNet, a lightweight, efficient traffic prediction model that captures traffic patterns with reduced complexity and incorporates target information for improved accuracy.

Contribution

The paper presents a novel dynamic memory network for traffic prediction that reduces computational complexity and integrates target-aware prediction methods.

Findings

Outperforms existing models on benchmark datasets

Achieves O(N) complexity in traffic pattern capturing

Effectively incorporates target information for trend prediction

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: https://github.com/wengwenchao123/PM-DMNet.