# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 1 Introduction  
The importance of maritime traffic in the shipping industry continues to increase because shipping activities are wide-ranging and directly affect the security, safety, and environmental and socio-economic factors [1]. Moreover, maritime traffic is impacted by rapid technological developments and various marine environments such as increases in ship size, the emergence of autonomous ships, marine tourism, and offshore wind farms [2, 3, 4]. Such changes in the marine traffic environment and interactions among multiple stakeholders in the marine space threaten the safety of ship passage [5, 6]. In this context, Korea is attempting to develop a national maritime traffic route to ensure the safety of navigation. A national maritime traffic route is a water area designated by the country as a route for ships [7] and is organically combined with maritime traffic facilities to ensure fast, safe, and convenient ship navigation [8]. For example, in Taiwan, the Changhua wind farm channel implements a two-way traffic scheme that separates the northbound and southbound traffic to preserve the existing customary traffic flow in offshore wind farms [9]. As another example, a main public shipping route was established along the complicated coast of Zhejiang, China, to ensure the safety of ships [10]. To improve the efficiency, sustainability, and environment friendliness of marine-resource use, multiple countries have adopted marine spatial planning (MSP) for maritime policy and decision making [11, 12]. Based on the MSP concept, marine use zones in Koreaare classified into nine types by purpose of use. The zone related to maritime traffic is defined as a port and navigation zones to ensure the maintenance of port functions and safe navigation of ships [13]. Most zones comprise port areas, fairways, traffic lanes, traffic separation schemes (TSS), anchorages, and specific sea areas for traffic safety (designated as _legal areas_ under the Maritime Safety Act) [14]. However, the customary traffic flow of ships along the coast is not preserved [15] and traffic density is assigned a smaller weight than the other evaluation items for designating ports and navigation zones [16]. Moreover, the grid resolution is extremely low to properly capture the traffic density when establishing a marine spatial plan. Therefore, an effective methodology for traffic-density zone mapping is important for a quantitative MSP-based evaluation of actual maritime traffic in the coastal waters of Korea. Maritime traffic analysts use automatic identification system (AIS) data, which are based on ship trajectory data, to understand the characteristics and distribution of maritime traffic and perform statistical and risk analysis [17; 18; 19]. It is a system for transmitting messages containing dynamic and static information from ships and for receiving messages from base stations and other ships [20; 21; 22]. Installation of AIS system is mandatory for all ships of 300 gross tonnages and upwards on international voyages, cargo ships of 500 gross tonnage and upwards not engaged on international voyages and passenger ships irrespective of size [23]. The AIS automatically informs ships of other ships' positions and identifications [24], which not only helps with collision avoidance but also provides trajectory information reflecting the density of vessels at sea [25]. The traffic flows of ships are not random but collectively form patterns. Therefore, methods that evaluate the density of maritime traffic and quantitatively extract the hotspots have become an important part of maritime intelligence services [26]. In addition to the recent development of sensing technology and storage and processing technologies for big data, voluminous AIS data have been accumulating in real time. Therefore, various studies such as ship densities, motion patterns, traffic predictions, and routes extractions are being conducted using AIS data [27; 28]. To begin, some studies have performed clustering algorithm based on AIS trajectories. [PERSON] et al.