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[2602.21757] Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Summary
The paper presents FORESEE, an efficient online learning method for traffic demand prediction that improves accuracy and reduces computational costs by utilizing past prediction errors.
Why It Matters
Accurate traffic demand forecasting is essential for intelligent transportation systems. The proposed method addresses the challenges of model retraining and performance degradation due to distribution shifts, making it a significant advancement in real-time traffic prediction.
Key Takeaways
- FORESEE improves traffic demand prediction accuracy by correcting forecasts using previous errors.
- The method avoids costly model retraining, making it computationally efficient.
- It effectively adapts to distribution shifts in urban traffic patterns.
- Extensive testing shows robust performance across various real-world datasets.
- The framework is designed for real-time application in dynamic urban environments.
Computer Science > Machine Learning arXiv:2602.21757 (cs) [Submitted on 25 Feb 2026] Title:Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction Authors:Xiannan Huang, Quan Yuan, Chao Yang View a PDF of the paper titled Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction, by Xiannan Huang and 2 other authors View PDF HTML (experimental) Abstract:Accurately predicting short-term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary conditions, their accuracy often degrades significantly when faced with distribution shifts caused by external events or evolving urban dynamics. Frequent model retraining to adapt to such changes incurs prohibitive computational costs, especially for large-scale or foundation models. To address this challenge, we propose FORESEE (Forecasting Online with Residual Smoothing and Ensemble Experts), a lightweight online adaptation framework that is accurate, robust, and computationally efficient. FORESEE operates without any parameter updates to the base model. Instead, it corrects today's forecast in each region using yesterday's prediction error, stabilized through exponential smoothing guided by a mixture-of-experts mechanism that adapts to recent error dynamics. Moreover, an adaptive spatiotemporal smoothing component propagates error signals across neighboring regions and ...