Recently, marine development plans such as offshore wind farms and marina port facilities have been established to use Korean coastal waters, and research on the development of operational ships such as autonomous ships and water-surface flying ships is being rapidly promoted. Since the marine traffic in Korean coastal waters is expected to increase, the government intends to construct a coastal maritime transportation network that connects Korean coastal waters to guarantee safe ship navigation. Therefore, this study used automatic-identification-system data analysis to obtain quantitative evaluation results on maritime traffic distribution characteristics and utilization levels for the entire Korean coastal waters in grid cell for greater consistency and compatibility. The characteristics of marine traffic distribution at a certain site in coastal Korean waters can be quantitatively examined using the findings of this study, and they may be used as grid-cell-type data-based information. Moreover, the vessel traffic index allows for extensive research while quickly understanding the present level of use of the passing ships by the sea area. In this regard, the findings of this study are expected to be useful for the future development of maritime transportation networks in Korean coastal waters.

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments.

Recent projections from marine transportation experts highlight an uptick in maritime traffic, attributed to the fourth industrial revolution’s technological strides and global economic rebound. This trend underscores the need for enhanced systems for maritime accident prediction and traffic management. In this study, to analyze the flow of maritime traffic macroscopically, spatiality and continuity reflecting the output of ships are considered. The course–speed (CS) model used in this study involved analyzing COG, ROT, speed, and acceleration, which can be obtained from the ship’s AIS data, and calculating the deviation from the standard plan. In addition, spatiality and continuity were quantitatively analyzed to evaluate the smoothness of maritime traffic flow. A notable finding is that, in the target sea area, the outbound and inbound CS indices are measured at 0.7613 and 0.7501, suggesting that the outbound ship flows are more affected than inbound ship flows to the liquidity of maritime traffic flow. Using the CS model, a detailed quantitative evaluation of the spatiality and continuity of maritime traffic is presented. This approach facilitates robust comparisons over diverse scales and periods. Moreover, the research advances our understanding of factors dictating maritime traffic flow based on ship attributes. The study insights can catalyze the development of a novel index for maritime traffic management, enhancing safety and efficiency.