Abstract
Vessel arrivals at maritime ports are often modeled by Poisson processes with independent increments, yet real traffic data often exhibits short-term autocorrelation. Using daily arrival data derived from the Automatic Identification System (AIS), we detect significant positive autocorrelation at short lags even after removing weekly seasonality. This temporal dependence may reshape port congestion dynamics, affecting how congestion emerges, persists, and dissipates. We treat port congestion as a threshold-exceedance recurrent failure event and assess port reliability under uncertainty through failure recurrence, congestion duration, and recovery dynamics, recognizing that sustained congestion itself creates additional risk by propagating delays, slowing recovery, and increasing vulnerability to further disruption. This connects the temporally autocorrelated arrival process directly to port resilience assessment and capacity planning under real traffic. To account for autocorrelation, we develop a delay-induced arrival model that combines independent per-vessel and fractional block-wise delay components to reproduce observed short-lag correlations. Discrete-event simulations of parallel berth queues show that the proposed fractional delay mechanism introduces two distinct statistical effects. Under moderate loads, reduced marginal variance smooths daily transit times and lowers congestion frequency. Under near-capacity loads, positive autocorrelation yields longer and more severe congestion with slower recovery. Thus, temporal dependence has a regime-dependent impact on port reliability: it stabilizes performance under moderate utilization, but amplifies tail risk near saturation by prolonging congested episodes and increasing their severity. Recognizing and monitoring such dependence is essential for reliability assessment under uncertainty and for designing capacity policies that reduce extreme congestion and recovery time.
•AIS data reveals vessel arrivals show short-term autocorrelation, not Poisson.•A delay-induced arrival model is proposed to capture short-lag dependence.•Reduced marginal variance smooths traffic, while autocorrelation amplifies tail risk.•Ignoring arrival dependence underestimates port vulnerability and recovery time.