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[2504.13961] CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Summary
The paper presents CONTINA, a method for predicting traffic demand with confidence intervals that adapt to changing conditions, ensuring valid predictions for traffic management.
Why It Matters
Accurate traffic demand predictions are essential for efficient transportation systems. CONTINA addresses the limitations of existing methods by providing reliable confidence intervals that adjust to real-world changes, enhancing operational planning and decision-making in traffic management.
Key Takeaways
- CONTINA offers adaptive confidence intervals for traffic demand predictions.
- The method improves upon existing models by ensuring valid coverage in dynamic environments.
- Real-world experiments demonstrate shorter and more reliable confidence intervals.
- The approach aids traffic management in developing robust operational plans.
- Code and datasets are made available for further research and application.
Computer Science > Machine Learning arXiv:2504.13961 (cs) [Submitted on 17 Apr 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee Authors:Chao Yang, Xiannan Huang, Shuhan Qiu, Yan Cheng View a PDF of the paper titled CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee, by Chao Yang and 3 other authors View PDF HTML (experimental) Abstract:Accurate short-term traffic demand prediction is critical for the operation of traffic systems. Besides point estimation, the confidence interval of the prediction is also of great importance. Many models for traffic operations, such as shared bike rebalancing and taxi dispatching, take into account the uncertainty of future demand and require confidence intervals as the input. However, existing methods for confidence interval modeling rely on strict assumptions, such as unchanging traffic patterns and correct model specifications, to guarantee enough coverage. Therefore, the confidence intervals provided could be invalid, especially in a changing traffic environment. To fill this gap, we propose an efficient method, CONTINA (Conformal Traffic Intervals with Adaptation) to provide interval predictions that can adapt to external changes. By collecting the errors of interval during deployment, the method can adjust the interval in the next step by widening it if the errors are too large or shortening it otherwise. Fur...
