###### Abstract  
This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems. maritime traffic safety assessment (MTSA); MTSA-related route; MTSA-related accident reduction rate; synthetic minority oversampling technique; random forest model 2024202432  
Initially, it was mandated that MTSA should be conducted for all marine development projects. However, there have been concerns regarding the need to perform MTSA for projects with minimal impact on maritime traffic safety. The government subsequently accepted this opinion and, in 2014, it refined and relaxed the criteria for MTSA-targeted projects. Since then, the scope of projects subject to MTSA has narrowed to areas where marine development projects are conducted in vessel traffic areas with ships over 100 m long. The specific scope of safety assessment-targeted projects is specified in the Enforcement Decree of the Maritime Safety Act [Appendix 2-3], and the details are shown in Table 1.  
While this system contributes to the prevention of marine accidents from the perspective of its effectiveness, there is a growing need for regulatory enhancements regarding marine development projects in the waterways of maritime routes for coastal passenger ships, which are categorized as safety blind spots. In particular, the majority (94%) of domestic coastal passenger ships are less than 100 m in length, resulting in the exclusion of marine development projects from safety assessment-targeted projects. Consequently, numerous near-accidents and safety hazards have been identified on completed bridges and in port facilities. Moreover, coastal passenger ships pose a higher risk of personal injury compared with other types of vessels. Therefore, it is necessary to expand the scope of safety assessments for the waterways of maritime routes for coastal passenger ships [3]. However, MTSA is regulatory in nature, and it is therefore essential to calculate the safety benefits relative to the increased economic burden on marine developers to ensure the validity of system improvements [4]. In numerous maritime safety-related studies, the occurrence of marine accidents is considered a crucial aspect of risk assessment [5]. The reduction in such accidents can be quantified by converting them into monetary values [6]. However, because of the relatively small number of marine accidents relative to the number of voyages made by coastal passenger ships, it is difficult to assess the probability of marine accidents using only statistical methods. In addition to statistical methods, machine learning (ML) can also be used to predict marine accidents based on various independent variables. There has been ongoing research that employs ML to predict accident risks for other modes of transportation, such as automobiles [7,8].