# Improving the Maritime Traffic Evaluation with the Course and Speed Model
## 2 Literature Review  
Collecting data through traffic surveys is a crucial step in estimating traffic volume. In the case of road traffic, the scope of the report and the entity responsible for its implementation are distinguished according to the relevant laws. Typically, traffic volume and movement are assessed by studying the origin and destination (O/D) traffic volume. This method involves surveying observation points, either manually or with machines, to extract data samples. The collected data are then compared with screen line survey results and household interviews to validate the survey's accuracy [2; 3]. However, the scope of movement for maritime transport is considerably broader than that of road traffic, rendering the same survey method inadequate owing to the significant restrictions on observation points at sea. Therefore, the IMO mandates that all passenger ships, including domestic cargo ships of 500 tons or more, irrespective of their size, and international service ships of 300 tons or more, must be equipped with an installed AIS [4]. Additionally, domestic vessels are required to install and operate a vessel pass (V-Pass) in compliance with the Fishing Vessels Act. This system automates port entry declarations for fishing boats, enabling rapid responses to marine accidents and facilitating data collection from ships traversing various oceanic areas, including domestic coasts. AIS holds high value in analyzing the data related to diverse maritime traffic as it is standardized by the International Telecommunication Union (ITU) and the International Electrochemical Commission (IEC). Moreover, it can be directly set up and operated on ships. Nevertheless, errors might arise due to incorrect global positioning system (GPS) settings during the initial setup [5]. These AIS data are used as the basis for a variety of assessments, such as automated maritime routing [6], or risk analyses related to maritime traffic, collision probability, and congestion levels. This study primarily focuses on researching the marine traffic assessment, specifically utilizing the AIS for maritime traffic [7; 8]. In a local context, a risk assessment model is developed to evaluate maritime traffic hazards in coastal waters. The model is formulated through a survey of domestic ship operators, a research endeavor commissioned by the Ministry of Land, Transport, and Maritime Affairs. Recognized as the potential assessment risk (PARK) model, it is employed for risk assessment in a domestic coastal setting. Several studies have adopted the PARK model for risk assessments. For instance, the collision avoidance algorithm was examined by implementing the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) using the PARK model [9]. Additionally, compared this model with a collision warning algorithm for small fishing boats [10]. In other countries, an Environmental Stress (ES) model [11] has been developed. This model provides a quantitative index of the shipbuilding difficulties faced by ship operators. The comprehensive aggregation of the ES value (ESA) comprises environmental stress due to surroundings (ESLs) and traffic environment stress induced by other ships (ESSs). This research utilizes ship maneuvering simulations and surveys to ascertain the risks associated with a ship's surroundings, distinguishing the risks based on surrounding ships and the topography. However, this assessment is limited to sea areas with a unidirectional traffic flow or wide sea areas without obstructions. To evaluate domestic application effectiveness, the research was conducted by comparing the risks perceived by pilots during on-site operations with the results from the ES model, and the results were aligned [12]. Furthermore, an analysis was performed using the ES model and the IALA Waterway Risk Assessment Program (IWRAP) of the International Association of Maritime Aid Organizations (IALA) to determine the risk factors of certain ships for offshore wind power plants. [13].