This study explores traffic patterns on Taiwanese highways during consecutive holidays and focuses on understanding Taiwanese highway traffic behavior. We propose a prediction-based detection method for finding highway traffic anomalies using reconciled ordinary least squares (OLS) forecasts and bootstrap prediction intervals. Two fundamental features of traffic flow time series -- namely, seasonality and spatial autocorrelation -- are captured by adding Fourier terms in OLS models, spatial aggregation (as a hierarchical structure mimicking the geographical division in regions, cities, and stations), and a reconciliation step. Our approach, although simple, is able to model complex traffic datasets with reasonable accuracy. Being based on OLS, it is efficient and permits avoiding the computational burden of more complex methods. Analyses of Taiwan's consecutive holidays in 2019, 2020, and 2021 (73 days) showed strong variations in anomalies across different directions and highways. Specifically, we detected some areas and highways comprising a high number of traffic anomalies (north direction-central and southern regions-highways No. 1 and 3, south direction-southern region-highway No.3), and others with generally normal traffic (east and west direction). These results could provide important decision-support information to traffic authorities.

翻译：本研究探讨台湾连续假期期间的高速公路交通模式，重点理解台湾高速公路的交通行为。我们提出一种基于预测的异常检测方法，利用协调的普通最小二乘(OLS)预测和自助法预测区间发现高速公路交通异常。通过向OLS模型添加傅里叶项、空间聚合（模拟区域-城市-站点地理分层结构的层级体系）及协调步骤，捕获交通流时间序列的两个基本特征——季节性与空间自相关性。该方法虽形式简洁，却能以合理精度建模复杂交通数据集。基于OLS的框架使其兼具高效性，避免复杂方法的计算负担。对台湾2019、2020及2021年连续假期（共73天）的分析显示，不同方向与高速公路的异常模式存在显著差异：我们检测到部分区域及高速公路存在高密度交通异常（北向-中部及南部区域-1号及3号高速公路，南向-南部区域-3号高速公路），而其他方向（东向及西向）整体交通正常。这些结果可为交通管理部门提供重要的决策支持信息。