Transformer-Based Multi-Horizon Traffic Forecasting in Bulk Port Environments

Abstract

Reliable multi-horizon traffic forecasts can improve operational planning at bulk ports, but the performance of data-intensive forecasting models depends on training-set size and forecast horizon. This study compares seven architectures—ARIMA, LSTM, GRU, a standard Transformer, PatchTST, iTransformer, and the Temporal Fusion Transformer—using aggregate vehicle counts and mean speeds generated by a statistically checked agent-based discrete-event simulation. Forecasts cover short (15–30-minute), medium (seven-day), and long (30-day) horizons. A preliminary 569-observation short-horizon experiment favored ARIMA; after expanding the simulated short-horizon series to 51,840 observations, all six deep models outperformed that baseline. For 15-minute vehicle-count prediction, PatchTST achieved R² = 0.4921 compared with ARIMA's R² = 0.3632. Mean R² varied non-monotonically across short, medium, and long horizon tiers (0.290, 0.517, and 0.325, respectively). The greatest transformer advantages emerged in longer-horizon speed forecasts, where PatchTST attained R² values of 0.9821 and 0.9741 at the medium and long tiers. Feature-ablation results suggested that the value of additional predictors depended on sample availability. A supplementary evaluation using independent DT-CARGO truck telemetry supported the advantage of deep approaches over ARIMA, although recurrent models slightly surpassed transformer variants on that external series. These findings support horizon- and data-aware model selection rather than a universal architectural ranking. Results based on simulation require validation in operational bulk-port settings.