Continual Traffic Forecasting via Mixture of Experts

Understand

The real-world traffic networks undergo expansion through the installation of new sensors, implying that the traffic patterns continually evolve over time.

• Incrementally training a model on the newly added sensors would make the model forget the past knowledge, i.e., catastrophic forgetting, while retraining the model on the entire network to capture these changes is highly inefficient.
• To address these challenges, we propose a novel Traffic Forecasting Mixture of Experts (TFMoE) for traffic forecasting under evolving networks.
• The main idea is to segment the traffic flow into multiple homogeneous groups, and assign an expert model responsible for a specific group.