DualCast: Disentangling Aperiodic Events from Traffic Series with a Dual-Branch Model

2024-11-27

arXiv.org

AI Summary

Existing traffic flow forecasting models overly rely on periodic patterns, rendering them ineffective at modeling aperiodic events—such as accidents—thus compromising prediction robustness. To address this, we propose a dual-branch decoupled architecture that decomposes traffic time-series signals into intrinsic spatiotemporal patterns and exogenous environmental context containing aperiodic events. We further design a cross-temporal attention module to jointly model periodic and aperiodic dynamics while capturing high-order spatiotemporal dependencies. Our method is plug-and-play compatible with mainstream models (e.g., STGCN, AGCRN). Extensive experiments on multiple real-world datasets demonstrate an average 9.6% reduction in prediction error, significantly improving responsiveness to突发事件 and enhancing generalization robustness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Traffic forecasting is an important problem in the operation and optimisation of transportation systems. State-of-the-art solutions train machine learning models by minimising the mean forecasting errors on the training data. The trained models often favour periodic events instead of aperiodic ones in their prediction results, as periodic events often prevail in the training data. While offering critical optimisation opportunities, aperiodic events such as traffic incidents may be missed by the existing models. To address this issue, we propose DualCast -- a model framework to enhance the learning capability of traffic forecasting models, especially for aperiodic events. DualCast takes a dual-branch architecture, to disentangle traffic signals into two types, one reflecting intrinsic {spatial-temporal} patterns and the other reflecting external environment contexts including aperiodic events. We further propose a cross-time attention mechanism, to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.

Problem

• Disentangles traffic signals into periodic and aperiodic events
• Addresses overlooking critical aperiodic events in traffic forecasting
• Reduces forecasting errors by capturing spatial-temporal relationships

Innovation

• Dual-branch framework disentangles traffic signals
• Cross-time attention captures spatial-temporal relationships
• Integrates with models reducing forecasting errors