# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 1 Introduction
grouped similar AIS trajectories using the Hausdorff distance and Douglas-Peaker algorithms and then identified traffic routes using the density-based spatial clustering of applications with noise (DBSCAN) algorithm [29]. Based on kernel density estimation (KDE) analysis, finally determined the width of the route and statistically evaluated the waypoints. Applying the Douglas-Peucker and DBSCAN algorithms, [PERSON] et al. divided each ship route into a turning section and a route leg for characterizing ship behavior [30]. [PERSON] et al. proposed a new ship-trajectory clustering method based on a Hausdorff distance algorithm and hierarchical DBSCAN (HDBSCAN) [31]. [PERSON] et al. applied a DBSCAN algorithm to identify the hotspots of ship traffic as an indicator for maritime safety operators [32]. Several other studies have analyzed ship density by using AIS data and grid cells. To gain geographic visualization and insights into shipping movements, [PERSON] et al. applied KDE to density analysis while accounting for ship velocities [33]. [PERSON] et al. conceptualized shipping density as both vessel and traffic density calculating the number of ships per unit area per unit time and thereby developed a density map [34]. [PERSON] et al. proposed a ship-size conversion and grid-based density model based on nonparametric KDE, which considers the effect of ship size on traffic safety [35]. Finally, there are study on the extraction of maritime traffic routes using AIS data. [PERSON] et al. analyzed AIS data using KDE and extracted the traffic routes using an image processing technique [36]. As another study, [PERSON] et al. extracted marine traffic routes in Korea coastal waters by dividing in 16 areas and selecting the top 50% of results of density analysis based on ship trajectories [37]. Most studies based on AIS data identify the traffic patterns and create routes using clustering algorithms, KDE, density analysis, and similar methods. However, the spatial extent of ports and other relevant areas is on the regional scale and the analytical data are collected over several days or months, insufficient for explaining the seasonal effect. Moreover, the visualization method cannot be quantified for map creation from the maritime traffic analysis result. Most of the ship density models in previous studies were based on the number of ships or AIS messages within each cell or adopted probabilistic analyses such as KDE. These analytical methods have specific limitations; for example, AIS-message intervals are irregular and the actual traffic conditions, such as the ship's moving speed and sailing time, cannot be captured. As no global standard method exists for density analysis and evaluation in the maritime traffic field [38], one must study and develop various types of methodologies based on the historical AIS data. This study proposes a new methodological concept of spatial-temporal density analysis using maritime traffic data to build Korea's national maritime traffic route. The voyage time of the ships in each cell is calculated by combining the ship occupancy time (the temporal concept) with the trajectory (spatial concept). This combination of concepts solves the problems caused by the irregular reporting intervals of AIS messages; in particular, it requires no data interpolation or parameter selection. Furthermore, as this new method can reflect the ship speed, it can accurately calculate the density similar with actual traffic situations. Through appropriate visualization and extraction of the density analysis results, this new method enables the final selection of the primary traffic route to be defined as the navigation area for ships in MSP. This basic principle and methodology for building a national maritime transport route will possibly contribute to navigation safety and marine accident prevention. Among the possible contributions are optimal route determination, passage planning, and vessel traffic services.