Safety Risk Evaluation by Vessel Type and Accident Category in an Archipelagic State: An Empirical Data Analysis from Indonesia

Maritime safety in archipelagic states is shaped by fragmented geography, uneven oversight capacity, and highly variable operating conditions, which together create non-uniform risk across vessel classes and routes. Indonesia’s thousands of islands generate complex navigation and logistics demands that require robust governance and infrastructure [1]. The Indonesian maritime domain, covering approximately 3.25 million km², is not merely a transit route but the primary artery for economic activity, inter-island connectivity, and resource extraction. However, the capacity to enforce maritime laws and maintain safety standards across such an expansive area is often strained, a challenge common to many developing archipelagic states [2, 3].

These geographic challenges are compounded by security and governance stressors (e.g., illegal fishing, piracy, and smuggling) that complicate enforcement and safety management. At the same time, rising dependence on maritime transport for trade and mobility increases traffic density, which can elevate accident exposure and operational pressures [4]. In Southeast Asia, this coupling of traffic growth and uneven compliance capacity has been associated with persistent safety problems, making risk differentiation (by fleet and operating area) more informative than aggregate national statistics.

The research problem arises from the persistence of accidents that appear patterned rather than random, indicating gaps in prevention and response. Prior studies link incidents to human factors, adverse weather, and infrastructural shortcomings [5], while high-density corridors such as the Sunda Strait are repeatedly identified as hotspots where frequent ferry movements amplify risk [6]. This evidence implies that both upstream prevention (seaworthiness, training, compliance) and downstream mitigation (search-and-rescue timeliness) should be evaluated to prioritize interventions that are realistically implementable.

Consequently, a reactive “one-size-fits-all” approach is unlikely to control risk effectively in an archipelagic setting where fleets differ in design, oversight, and operating environments. A proactive, data-driven safety strategy requires identifying which vessel–accident–location combinations produce disproportionate harm and where response performance may further shape outcomes. Such a foundation enables evidence-based prevention and operational planning that can be prioritized under limited resources [7].

Established risk assessment frameworks provide systematic ways to translate incident data into control options. Formal Safety Assessment (FSA), for example, structures hazard identification, risk evaluation, and selection of risk control measures using empirical evidence [8]. In the Indonesian context, FSA-style logic is most useful when it is operationalized into differentiated priorities (e.g., by fleet type and operating area) rather than applied as a generic national checklist.

Goerlandt and Montewka [9] emphasized combining quantitative and qualitative reasoning to tailor maritime risk analysis to sector-specific conditions. Similarly, the Multi-State Maritime Transportation System Risk Assessment framework offers a more granular analysis by considering partial failure scenarios, which provides a more robust evaluation of operational vulnerabilities [10]. Building on these foundations, this study positions risk profiling as a decision-support instrument: not only characterizing accident patterns but also informing where prevention and response capacity (SAR) should be strengthened first.

Prior literature consistently highlights human factors and adverse weather as dominant contributors to marine incidents. Human reliability studies identify miscommunication, decision errors, and monitoring failures as recurring mechanisms [11, 12], while weather-related hazards motivate integrating forecasting into operational planning [5, 11]. A second, policy-relevant layer is regulatory applicability and compliance: international instruments (e.g., SOLAS, STCW, and the ISM Code) and their national transposition can produce different compliance realities between fleets—particularly between smaller fishing vessels and more regulated passenger/cargo operations. Despite this, a gap remains in combining (i) large-scale accident evidence, (ii) fleet- and geography-specific prioritization, and (iii) operational response performance (SAR timeliness) into a single decision-support profile for an archipelagic state [13].

Therefore, this study evaluates maritime safety risk by vessel type, accident category, and location using Indonesian accident records (2018-2021), and extends the analysis to operational mitigation capacity by summarizing BASARNAS handling times (reporting → dispatch → arrival) to contextualize risk prioritization. The contribution is a differentiated risk-and-response profile that supports targeted regulation and enforcement rather than generalized assumptions. The study tests the hypotheses that (H1) accident categories differ significantly across vessel types and (H2) sea-water and inland-water environments exhibit distinct risk profiles, and uses the resulting evidence to propose risk-based, regulation-aligned, and resource-aware intervention priorities. To maintain interpretability and avoid over-claiming causality, the approach focuses on empirically observable associations and provides a basis for phased implementation planning and periodic updating as new data become available.

The results are presented in three parts: accident-type distribution, vessel-type distribution, and spatial concentration (sea-water vs. inland-water). A separate subsection reports SAR response-time performance metrics to support operational implications without implying causality.

The analysis of 1,634 recorded incidents indicates a persistent national safety challenge. Accident categories were first summarized descriptively (Figure 1). Capsizing/Sinking is the dominant category (779 incidents; 47.7%), followed by Fire/Explosion (235; 14.4%) and Collision/Allision (205; 12.5%). Additional categories include Grounding (7.9%), Fatality/Workplace Injury/Person Overboard (7.6%), and Machinery/Equipment/Structural Damage (6.6%).

Figure 1. Distribution of maritime accident types in Indonesia (2018-2021)

The dominance of Capsizing/Sinking (47.7%) shifts attention beyond navigational conflicts to stability, seaworthiness, and operational integrity under adverse conditions. The two extreme scenarios Fishing Vessel for Capsizing/Sinking (388) and Passenger Ship with Capsizing/Sinking (111) indicate that these fleets warrant the highest priority for preventive controls. Rather than framing this as “unequivocal” causal proof, we interpret it as strong empirical prioritization evidence that a uniform regulatory approach is insufficient. This supports a differentiated framework that aligns controls with vessel characteristics and applicable safety governance mechanisms [21], including Indonesia’s implementation of international obligations (SOLAS/STCW/ISM) where relevant and fleet-appropriate standards for fishing vessels where coverage differs.

The fishing sector’s risk concentration supports targeted assessment and control selection using structured approaches such as FSA [22, 23]. The finding that passenger-ship capsizing/sinking is also Very Frequent in this dataset indicates that the issue is not limited to rare catastrophes and warrants urgent attention through measurable controls, such as mandatory stability verification, stricter inspection regimes, and crew competency reinforcement consistent with passenger-vessel safety expectations [24].

Location-based results distinguish open-sea and riverine risk landscapes. The Java Sea functions as the primary multi-vessel hotspot (High/Extreme across fishing, cargo, and passenger fleets), while the South China Sea and East Indian Ocean risk is concentrated primarily in fishing operations. In contrast, inland waterways (Mahakam and Musi) are dominated by tug/push-boat–barge and speedboat incidents, where congestion and navigational complexity may be more influential than meteorological exposure. This differentiation supports geographically tailored strategies: offshore fishing corridors require seaworthiness and weather-readiness controls, whereas inland waterways require traffic management and operational oversight appropriate to riverine conditions. The approach is potentially transferable to other archipelagic settings, but local fleet composition, governance capacity, and traffic patterns will shape the resulting prioritization [25].

Adverse weather is a plausible trigger for many capsizing events; however, the high frequency suggests it can also amplify underlying vulnerabilities (seaworthiness deficiencies, operational decision-making, and safety culture). The decision to operate in marginal conditions may reflect economic pressures, shifting the problem from purely environmental exposure to human and organizational factors [26] (This perspective is consistent with evidence that unsafe practices can arise from gaps in training, procedures, and safety culture [27].

The pattern of weather-associated losses highlights the need for interventions that address both information (forecasting/alerts) and behavior (decision-making and preparedness). Practical measures include targeted training, emergency drills, and safety management routines that improve readiness and reduce risk-taking under pressure [28], alongside stakeholder collaboration to strengthen safety culture and compliance incentives [29]. Where passenger and cargo fleets are subject to international safety expectations (e.g., STCW/ISM-related competency and management systems), enforcement should focus on demonstrable compliance; for fishing fleets, interventions should be adapted to fleet structure and capacity while maintaining measurable safety outcomes.

The risk matrices provide a basis for policy prioritization, supporting a shift from broad, uniform rules toward risk-based targeting. The identification of extreme/high-risk scenarios and locations enables strategic allocation under resource constraints. To strengthen operational justification, prioritization should integrate both accident concentration and BASARNAS response performance (dispatch delay and end-to-end arrival time) to identify where additional SAR readiness is likely to yield the greatest benefit. In practice, high-risk corridors such as the Java Sea warrant combined measures: prevention campaigns, compliance checks, and response-capability optimization. For vulnerable fishing fleets, improved storm-warning dissemination and real-time communication remain key preventive options [30].

For passenger vessels, the observed frequency of capsizing/sinking supports stronger inspection and competency assurance, consistent with international safety practice and national enforcement obligations. For inland waterways, interventions should emphasize traffic management, routing, and operational control suited to riverine congestion. To address feasibility and cost concerns, a simple multi-criteria prioritization (risk level × consequence proxy × SAR response-time performance × feasibility) can be used to select “quick wins” (0–6 months), mid-term actions (6–18 months), and regulatory refinements (18–36 months). Continuous monitoring is essential so that the prioritization remains valid as traffic patterns, fleet composition, and response capability evolve [31].