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This study examines the Taiwanese highway traffic behavior on consecutiveholidays, with the aim of evaluating the Taiwanese freeway bureau's trafficcontrol and management strategies. We propose a prediction-based detectionmethod for finding highway traffic anomalies using reconciled ordinary leastsquares (OLS) forecasts and bootstrap prediction intervals. Two fundamentalfeatures of traffic flow time series -- namely, seasonality and spatialautocorrelation -- are captured by adding Fourier terms in OLS models, spatialaggregation (as a hierarchical structure mimicking the geographical division inregions, cities, and stations), and a reconciliation step. Our approach,although simple, is able to model complex traffic datasets with reasonableaccuracy. Being based on OLS, it is efficient and permits avoiding thecomputational burden of more complex methods. Analyses of Taiwan's consecutiveholidays in 2019, 2020, and 2021 (73 days) showed strong variations inanomalies across different directions and highways. Specifically, we detectedsome areas and highways comprising a high number of traffic anomalies (northdirection-central and southern regions-highways No. 1 and 3, southdirection-southern region-highway No.3), and others with generally normaltraffic (east and west direction). These results could provide importantdecision-support information to traffic authorities.

Assessing Taiwanese Traffic Policy on Consecutive Holidays through Forecast Reconciliation and Prediction-based Anomaly Detection Techniques