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. Furthermore, we theoretically prove that the coverage of the confidence intervals provided by our method converges to the target coverage level. Experiments across four real-world datasets and prediction models demonstrate that the proposed method can provide valid confidence intervals with shorter lengths. Our method can help traffic management personnel develop a more reasonable and robust operation plan in practice. And we release the code, model and dataset in \href{ https://github.com/xiannanhuang/CONTINA/}{ Github}.

翻译：准确的短期交通需求预测对交通系统运行至关重要。除点估计外，预测的置信区间亦极为重要。共享单车调度与出租车派单等交通运营模型均需考虑未来需求的不确定性，并以置信区间作为输入。然而，现有置信区间建模方法依赖严格假设（如交通模式恒定与模型设定正确）以保证覆盖度，其提供的置信区间在动态交通环境中可能失效。为填补此空白，本文提出高效方法CONTINA（自适应保形交通区间），可提供适应外部变化的区间预测。该方法通过收集部署期间的区间误差，在下一步动态调整区间宽度：误差过大时拓宽区间，反之则收窄区间。此外，我们从理论上证明了该方法提供的置信区间覆盖度收敛于目标覆盖水平。在四个真实数据集与预测模型上的实验表明，所提方法能以更短的区间长度提供有效的置信区间。本方法可帮助交通管理人员制定更合理、更稳健的运营方案。相关代码、模型与数据集已发布于\href{ https://github.com/xiannanhuang/CONTINA/}{ Github}。