Forecasting Port Access Traffic Under Temporal Heterogeneity: A Leakage-Free Evaluation of Conditioning and Fixed Model Assignment

Abstract

Background: Port access traffic combines pronounced calendar structure with substantial day-to-day variability, raising the question of whether temporal heterogeneity should condition a common forecasting configuration or support model specialization. Methods: Daily car and freight-truck arrivals at an urban non-containerized port were forecast at 1-, 7-, and 14-day horizons using statistical, machine-learning, and composite models under a leakage-free rolling-origin protocol. A unified calendar-conditioned ensemble was compared with a prespecified regime-based model assignment, with development, test, and temporal stress-test periods kept distinct. Results: The unified ensemble achieved next-day R2 values of 0.75 for trucks and 0.80 for cars on the held-out 2018 test period and 0.73 and 0.75, respectively, under the 2019 temporal stress-test. Model preference varied jointly with temporal regime, forecast horizon, and evaluation period. Although fixed assignment improved car accuracy on the 2018 test period, the model–regime dominance required to justify it was not sufficiently stable across horizons and evaluation periods. Conclusions: Calendar conditioning provides a robust strategy for the present forecasting problem. More generally, fixed regime-based specialization should be supported by sufficiently stable model–regime dominance rather than inferred from identifiable temporal heterogeneity alone.