  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.
