Luling Zeng

The Changshan Waterway is a key passage connecting the Bohai Sea and the Yellow Sea in northern China, where complex navigational conditions and heavy vessel traffic place increasing pressure on navigational safety and operational efficiency. To quantitatively assess the throughput capacity of this waterway, this study analyzes vessel traffic characteristics using Automatic Identification System (AIS) data and develops a simulation-based capacity evaluation model implemented in AnyLogic. The results indicate that the current theoretical annual throughput capacity of the Changshan Waterway is approximately 248,000 vessels, with an overall capacity saturation level of 66.4%, approaching the critical operating threshold. On this basis, optimization strategies focusing on vessel speed regulation and longitudinal spacing management are proposed to further enhance the throughput capacity of the waterway.

Economic evaluation of Vessel Traffic Service (VTS) has long relied on sampling-based observations and average-cost assumptions, yet limited attention has been paid to whether evaluation frameworks themselves shape how the value of digital maritime governance is identified and represented. Drawing on full-sample vessel trajectories and dispatch records from a major Chinese hub port during 2018–2020, this study develops a parallel comparative design to compare a traditional sampling-based framework with a full-sample evaluation approach under identical traffic conditions. The results show that differences in evaluation frameworks affect not only the overall magnitude of estimated benefits, but also their composition. Compared with the traditional approach, the full-sample framework generates substantially higher estimates of economic value and assigns greater importance to routine coordination, where repeated small operational adjustments accumulate over time. By contrast, weather disruptions and channel-constrained coordination become relatively less prominent. These differences appear to be associated with variation in temporal benchmarks, sample coverage, and cost assignment strategies. Rather than identifying a single “correct” valuation, the study highlights the sensitivity of VTS economic assessment to methodological assumptions and suggests that, in digitally coordinated public infrastructure systems, evaluation frameworks may shape which forms of governance value become visible and incorporated into policy and investment decisions.

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments.