A voyage-level threshold analysis of planned turnaround vulnerability in cruise ports from an AIS big data perspective

IntroductionCruise port operations are facing increasingly significant challenges in departure reliability amid frequent climate variability, port congestion, supply chain disruptions, and geopolitical risks. Unlike existing studies that mainly explain cruise delays from the perspectives of weather conditions, external shocks, or supply chain risks, this paper focuses on whether port turnaround planning itself may constitute an endogenous source of vulnerability.MethodsTo identify this issue, this study uses the daily AIS data for the United States released by Marine Cadastre. These data are combined with planned berthing information, weather data, and vessel characteristics to construct a sample of 3,238 cruise ship–port–day observations. The study defines planned turnaround time as a time-based buffer resource in voyage-level operations. It then applies a fixed effects model, a threshold regression model, a half-hour interval model, mechanism tests, heterogeneity analysis, and moderation analysis to systematically examine the impact of planned turnaround time on departure reliability and its operational boundaries.ResultsThe results show a significant threshold-type nonlinear relationship between planned turnaround time and departure delay. An 8.5-hour threshold is identified as the main data-driven cutoff, while a meaningful buffering effect is mainly observed within 8 hours. The interval of 8.0–8.5 hours is better viewed as a transition range where marginal benefits begin to decline. Mechanism tests indicate that actual berthing service time is the most stable primary transmission channel of planned turnaround vulnerability, while port queuing pressure and berth occupancy pressure further modify the marginal effect of planned turnaround time.DiscussionThis study moves the analysis of cruise departure delays from explanations based on exogenous shocks to the identification of endogenous vulnerability. By revealing that planned turnaround time itself can become a source of risk under specific threshold conditions, these findings complement existing resilience literature that has primarily focused on external drivers. It provides new micro-level evidence for understanding the resilience of cruise port operations, optimizing schedule planning, and improving port coordination efficiency.