In response to the demand for marine space utilization and changes in the development environment, countries, including Korea, are establishing marine spatial plans tailored to their national circumstances and are designating marine use zones for management (Chae, 2009). In Korea, under the 2019 Marine Spatial Planning policy, unrestricted use and development activities across the country’s waters were limited. Nine types of marine use zones were established based on a comprehensive assessment of the marine ecosystem's value and the development potential of these spaces. These zones ensure that marine areas are used and developed in accordance with their specific characteristics. The marine use zone designated to prioritize the safe navigation of vessels is referred to as the Port and Navigation Zone. However, as of 2019, most of the designated and managed Port and Navigation Zones cover only about 4.3% of the total territorial sea area, which is quite limited, and their locations are confined to currently designated navigation routes or waters within port boundaries. Belgium established a marine spatial plan in 2020, designating zones for seven sectors, including biology, energy, ports, fisheries, and defense. The port zones are designed to maintain a safe distance from energy zones, such as offshore wind farms, to ensure vessel safety. Germany introduced a similar plan in 2021, designating zones for eight sectors, such as navigation, wind energy, and fisheries. Navigation and wind energy zones, in particular, are divided into core zones, buffer zones for safety, provisional zones for future development, and conditional zones for further demand analysis.

The maritime traffic environment in coastal waters is expected to become more complex due to future marine development plans (such as offshore wind farms) and new vessel operations, such as autonomous ships. As a result, proactive efforts, including policy development and detailed research, are needed to address changes in navigation, ensure vessel safety, and reduce maritime accident risks. In relation to government policies for establishing maritime traffic networks, the first step to ensuring safe navigation routes is to thoroughly review the distribution of vessel traffic and navigational conditions in domestic coastal waters (OPNT, 2024). This will allow for a detailed understanding of the maritime traffic characteristics in each area, which will serve as the foundation for setting up appropriate maritime traffic routes (Kim, 2021; Yoo et al., 2019).

When constructing infrastructure, such as offshore wind farms, in coastal waters, it is crucial to understand maritime traffic characteristics during the site selection process, to avoid overlapping with major traffic routes or high-density areas, thereby ensuring unobstructed maritime traffic flow.

There are numerous case studies on securing foundational data, such as assessing the adequacy of maritime traffic systems, through quantitative analysis of current traffic volume, maritime traffic patterns, and density based on AIS data. However, an appropriate evaluation model is lacking for selecting suitable sites for maritime traffic routes and offshore wind farms, as well as for planning maritime area utilization, such as visualized data by grade based on vessel traffic distribution and maritime traffic utilization levels by area. In particular, when conducting GIS-based vessel density analysis, it is possible to overlay, visualize, map, and extract maritime traffic information using vessel trajectory data. However, limitations arise due to variations in accuracy based on the density analysis method used, such as point, transit, or line density, and due to the effects of vessel speed and cell size settings. Additionally, discrepancies with the actual navigation environment may occur due to the influence of vessel size. Therefore, this study aims to develop and evaluate a suitability assessment model for maritime traffic routes that considers the spatio-temporal navigational environment characteristics of coastal waters. Based on the current maritime traffic conditions in Korea, this model seeks to establish optimal maritime traffic routes that ensure safe vessel navigation and support the efficient planning of coastal area utilization.

The study focuses on the complex interaction between vessel characteristics and geographical features in coastal waters, highlighting the importance of spatio-temporal analysis for assessing maritime traffic conditions. It identifies three main objectives:

1. To establish optimal maritime traffic routes by collecting detailed data on vessel types, Length Overall (LOA), Speed Over Ground (SOG), and vessel transit frequencies, as well as environmental factors such as currents and waves.

2. To assess traffic routes and congested areas in advance to aid in site selection and the effective use of maritime areas, particularly for infrastructure, such as offshore wind farms and marinas.

3. To input spatio-temporal operational and environ -mental characteristics into a suitability evaluation model, allowing for a visual representation of maritime traffic utilization levels based on user requirements.

The Automatic Identification System (AIS) data transmitted and received by ship AIS equipment consists of static information, including the ship's name, call sign, IMO number, Maritime Mobile Service Identity (MMSI) number, and ship type; dynamic information, including the ship's position (longitude/latitude), speed, and course over a period of time; and navigational information, including the ship's draft and cargo information (Hwang et al. 2016). Accordingly, AIS data is utilized as essential information in various studies related to the analysis of maritime traffic conditions in target sea areas, such as ports and shipping routes, both domestically and globally. Since 2020, the Korean government has been conducting safety assessments of ship routes based on AIS data to develop a systematic maritime traffic network. As a result, maritime traffic routes have been proposed according to the characteristics and intended uses of domestic maritime traffic, providing foundational data for establishing and implementing various policies for maritime traffic network construction and operation (Kim et al., 2020; Cho et al., 2023). The study has utilized AIS data collected over four seasons from Korea's coastal waters to extract maritime traffic routes and identify networks, through a quantitative big data analysis. This study also extracted traditional maritime traffic flow based on the top 25% of ship density (Kim et al., 2022). Another case included comparing and reviewing changes in navigators' risk levels resulting from increased maritime traffic and route width, using the Environment Stress (ES) Model, a risk assessment model, based on AIS data from the waters near Gunsan Port (Kang et al., 2022). Additionally, a case study that used AIS data from the waters near Mokpo Port performed a comparative analysis of the spatial flow of maritime traffic and the traffic network by time of passage (Oh et al., 2020). There is also a case where the DBSCAN algorithm, a machine learning clustering method, was used to analyze ship traffic patterns based on AIS data from the waters near Busan New Port. This study derived and presented the range of changes in ship positions, speeds, and courses over a designated time series (Lee et al., 2021).

Internationally, studies have been conducted that have investigated the complexity and spatial distribution of target sea areas using AIS data from ships in China (including Shenzhen) and Singapore. These studies designed methods for estimating both ship traffic volume and maritime traffic complexity models that reflect ship characteristics and the speed of ships in accident-prone sea areas (Wen et al., 2015; Liye et al., 2019).

Spatiotemporal pattern analysis is a type of Exploratory Spatial Data Analysis (ESDA) and serves as a useful method for identifying characteristics from large amounts of spatial data (KRIHS, 2011). Many phenomena tend to be distributed around specific locations in a given space, and these characteristics appear as distinct spatial patterns. This is called spatial autocorrelation, and it has been applied in cases such as preventing the spread of infectious diseases or preventing crime.

In Korea, after the Hebei Spirit oil spill incident in December 2007, researchers conducted an analysis on the spatiotemporal change patterns of oil pollution near the waters of Mallipo to assess and forecast the progression of the oil contamination. The study presented results that compared the temporal and spatial change patterns of oil concentrations. Over the course of four years, the rate of decrease in oil concentration during the winter was twice as fast as in the summer throughout the Mallipo area (Kim, 2012). Meanwhile, although point, line, and area-based spatial data have been available for some time, research using a grid system is essential for understanding the accuracy, characteristics, and trends based on a certain scale, as well as advancing related studies and establishing policies. Accordingly, a proposal for a national grid system for an integrated national grid data management system was presented, which included the format and methods for providing grid data (Kim et al., 2015). Additionally, to consider the burden on navigators in different sea areas, Korea's coastal waters have been divided into eight regions, and key evaluation criteria (average number of foggy days, route complexity, maritime traffic volume, and hazardous cargo handling amount) were established. A case study also evaluated the safety of ship traffic using Fuzzy Logic and the Choquet integral method (Keum et al., 2006). In one case, the waters near Busan Port were divided into a grid with 6-minute latitude and longitude intervals, and a Marine Traffic Risk Index model was proposed, which calculated risk indices by synthesizing risk indicators, evaluation criteria, and weighting factors (Park, 2021). Internationally, a study analyzed the temporal and spatial evolution of ship traffic and types in the Canadian Arctic between 1990 and 2015. The study presented patterns and trends in its findings and proposed them as foundational data for policy decisions regarding the development of maritime transportation across the Arctic (ARCTIC, 2009).

To evaluate the maritime route suitability for different types of vessels throughout the domestic coastal waters, the distribution of ship length, speed, and transit frequency has been visualized for cargo ships (T₁) and tankers (T₂), which comprise a large proportion of the total AIS data and follow relatively conventional maritime traffic patterns. Additionally, based on the spatio-temporal analysis results, the maritime traffic utilization level for each sea area according to the MTSI, along with the results of the maritime route suitability assessment model, have been presented. In this study, the maritime traffic utilization levels based on the MTSI are presented as follows to interpret the analysis results from the maritime route suitability assessment model. Levels 1 to 5 indicate utilization from "Extremely Low" to "Low," while Levels 6 to 10 indicate "High" to "Extremely High."

In the case of T₁, the analysis revealed that various patterns and segments with high maritime traffic utilization levels (SAMR Level 6-10) are present across the East, West, and South Seas, based on coastal navigation characteristics. For T₂, the analysis showed that maritime traffic utilization levels are higher in the West and South Seas compared to the East Sea. From the analysis of maritime traffic utilization levels, it was determined that it is possible to identify optimal maritime routes and appropriate sea areas for facility installations. The analysis results using the maritime route suitability assessment model for T₁ and T₂ are presented in Table 4. The X and Y axes of the graph represent the number of grids.

The analysis results of maritime traffic utilization levels using the SAMR model on designated routes (Ongdo Fairway, N. Maemulsudo Fairway, S. Maemulsudo Fairway, and Hongdo Fairway), located in domestic coastal waters with a relatively large number of islands, complex coastlines, and active operations of vessels ranging from large to small, including fishing boats, are presented in Table 5. The X and Y axes of the graph represent the number of grids.

Most of the designated routes showed higher levels of maritime traffic utilization over a wider area for T₁ compared to T₂. However, the entry and exit sections of the Sindo Fairway, located near the Ongdo Fairway, showed higher maritime traffic utilization levels for T₂ because of the frequent passage of hazardous cargo vessels, a characteristic of Daesan Port. The southern waters of Heuksando Island, located near the N. Maemulsudo Fairway, showed higher utilization levels for T₁, as vessels travel offshore to areas such as China. Similarly, the southern waters of the Hongdo Fairway showed higher utilization levels for T₁ because vessels move offshore from Busan and Ulsan Ports. Since 2020, the government has been conducting research into systematic maritime traffic network construction plans, considering the changes in ship traffic conditions due to the construction of various marine facilities (such as offshore wind farms) and the increased use of maritime spaces in domestic coastal waters, as well as the potential future use of coastal waters due to the development of autonomous ship technology. The primary research method involves employing computer algorithms and GIS programs based on the navigation environment and maritime traffic flow in domestic coastal waters (Son et al. 2019). This approach includes spatio-temporal density analysis and isodensity mapping for the spatial analysis of maritime routes, ultimately leading to the extraction of maritime route areas. Based on the characteristics and intended use of domestic maritime traffic, a total of four proposed maritime routes were derived, and the details are as follows. The wide area maritime route refers to the maritime traffic route which connects to the Traffic Separation Scheme (TSS) designated in the coastal waters of Korea, forming the main route with the highest traffic volume, circulating near the center of the country. The feeder maritime route and passenger ship maritime route refer to traffic routes for vessels using coastal and fishing ports, mainly used by ships navigating between islands and small harbors. The port access route refers to routes such as harbor entrances or legally designated routes, including those designated under the TSS.

The international navigation maritime route is located outside the main traffic routes. The results of overlaying the main maritime traffic routes on T₁, showing varying levels of maritime traffic utilization across different sea areas, are shown in Table 6. The X and Y axes of the graph represent the number of grids. When deriving maritime traffic routes, priority was given to segments with high maritime traffic utilization levels (SAMR Level 6-10), considering the major maritime traffic patterns of each sea area.

The currently operating offshore wind farms and the planned construction sites are mostly located within the territorial waters of the West and South Seas or within the EEZ areas, and they appear to overlap with long-established conventional maritime traffic flows in domestic coastal waters. Based on the maritime traffic characteristics of T₁, the suitability of maritime routes was evaluated by applying the MTSI and weights according to operational and environmental characteristics.

This evaluation was applied to the current offshore wind farm and wind measurement device locations to assess suitable sites, and the analysis results were detailed.

In the case of T₁, a considerable number of segments with high maritime traffic utilization levels (SAMR Levels 6-10) in the East, West, and South Seas were found to overlap with the locations of offshore wind farms and wind measurement devices. In the East Sea, overlapping segments were identified in the waters connecting offshore areas such as Russia from Ulsan Port and Busan Port; in the West Sea, near Anmado; and in the South Sea, near Jindo and Chodo. A visualization comparing the current status of offshore wind farms and wind measurement devices with the results of the maritime route suitability assessment based on T₁ is shown in Fig. 4. The X and Y axes of the graph represent the number of grids.

When installing offshore wind farms, it is important to plan transit or detour routes based on the SAMR results and to consider reducing the scale of the complex. Facilities should be restricted in areas with SAMR Levels 8 and above, and suitable locations should be reassessed.

In the case of domestic coastal waters, the characteristics of ships passing through these waters vary over time, and the geographic characteristics of each sea area also play a complex role. Therefore, spatio-temporal analysis is crucial for identifying the key maritime traffic characteristics of each area. Additionally, weather conditions such as tidal currents, waves, and fog, as well as operational environmental factors like VTS areas, significantly impact maritime traffic. Accordingly, each maritime route within the maritime traffic network must be designed with comprehensive consideration of both spatio-temporal operational and environmental characteristics to properly adapt to the conditions of domestic coastal waters.

This study analyzed spatio-temporal operational and environmental characteristics using ship AIS data and provided quantitative data on ship traffic distribution and utilization levels in the form of grid-based data. A SAMR incorporating the MTSI was developed. Furthermore, through the maritime route suitability assessment model, we established optimal maritime routes that ensure the safe navigation of ships and presented the validation and application results to enable efficient planning for the use of domestic coastal waters.

When using the maritime route suitability assessment model developed in this study based on AIS data, the maritime traffic utilization levels of each sea area, which comprehensively consider spatio-temporal and operational and environmental characteristics, can be visualized in a way that is easy for non-experts to understand. Additionally, it allows for the quantification of physical values reflecting the traffic characteristics of vessels in each sea area. In other words, the analysis results of ship traffic distribution and patterns by sea area based on AIS data enable the quantitative assessment of maritime traffic utilization levels in domestic coastal waters according to the MTSI. The grid-based data format is also efficient in terms of compatibility and connectivity with future studies.

Moreover, by using the maritime route suitability assessment model that considers spatio-temporal operational and environmental characteristics, it is possible to compare and verify proposed maritime route plans with offshore wind farm and wind measurement device installation plans. This will help establish optimal maritime routes that ensure the safe navigation of ships and contribute to efficient planning for the utilization of coastal waters.

Nevertheless, there are still limitations in this study. Since the results derived from the MTSI and SAMR are based on the distribution of vessel type, LOA, SOG, and transit frequency using AIS data from ships operating in domestic coastal waters, the analysis results are relative to the scope of domestic coastal waters. Therefore, if the characteristics of foreign waters and vessels are to be considered, an absolute analysis of ship traffic distribution characteristics and their integration into the model are necessary. Additionally, it is important to incorporate more detailed operational and environmental characteristics by calculating weights based on navigational situations, such as head-on, overtaking, and crossing situations in each sea area. Lastly, to enhance the reliability of the model presented in this study, it is required to include comparative validation with maritime traffic volume and accident frequency. This aspect is currently being pursued by the author, and more reliable results will be provided in future studies.