Maritime ports often model vessel arrivals as cyclical or Poisson processes, yet real traffic exhibits short-term autocorrelation that clusters arrivals and reshapes congestion dynamics. Using AIS-based daily arrival data, we detect significant positive autocorrelation at short lags even after removing seasonality, confirming persistent clustering. To account for this behavior, we develop a delay-driven arrival model that combines independent and block-dependent delay components to reproduce observed short-lag correlations and characterize their distributional properties. Discrete-event simulations of parallel berth queues show that under moderate loads, autocorrelation smooths daily transit times and reduces congestion frequency, whereas under near-capacity loads, it yields fewer but longer and more severe congestion with slower recovery. Short-term arrival dependence thus acts as a double-edged mechanism in port operations—stabilizing throughput at moderate utilization but amplifying risk near saturation. Recognizing and monitoring such dependence is essential for accurate reliability assessment and for designing adaptive service capacity to mitigate its adverse effects.

Zhang Zihan is a Ph.D. candidate in the Department of Industrial Systems Engineering and Management at the National University of Singapore, supervised by A/Prof. Zhisheng Ye. Her research interests include food supply chain, maritime network and system resilience. She received her Bachelor’s degree from Tsinghua University in 2020 and Master’s degree from Cornell Tech in 2021.