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

Analyzing Taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques