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Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.

Ship retrofitting is one alternative solution to extend a ship’s life. Several component replacements were performed in the retrofitting process, remodelings to the ship’s main dimension, and state-of-the-art equipment and technology were added. As one of the ships that require regular renewal, especially in research equipment, retrofit processes are often carried out on research vessels. The current problem in the retrofit process for research vessels, especially in Indonesia, is that retrofits are not carried out with established methodology and planning. Thus, some retrofit projects are limited to major repairs and do not extend the ship’s life or performance. To solve that problem, a developed methodology for ship retrofitting was proposed, which consists of selecting the ship and the type of retrofit and its components, determining retrofit requirements, and designing the ship based on the retrofit project. In the design process, a modified spiral design is explicitly used for the retrofit process with some alterations compared with the conventional one, which is focused on analyzing the suitability and availability of space, consumable and tank requirements, analysis of power requirements, risk analysis, and project execution plan. A case study of this developed methodology has been undertaken in the concept design phase of RV Baruna Jaya 1’s retrofit, and the results show that the methodology is considered helpful as an approach for ship retrofitting. Moreover, several considerations were also obtained from the concept design stage and had to be analyzed at the following design stage to meet the retrofit design requirements.

Failure of the braking system is one of the factors causing traffic accidents, therefore periodic testing of goods transport vehicles is very important. In fact, the incidence rate is still very high despite routine testing. Standard Operating Procedures (SOP) for periodic testing must be updated to reduce the risk of possible accidents. Therefore, procedures for updating the SOP for periodic brake system testing are presented in this article. The Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods were applied based on accident investigation data from the National Transportation Safety Committee (NTSC) from 2017 to 2022. FTA is used for risk identification, while FMEA is used for risk analysis to find the highest-risk failure cases. The results of our analysis showed that 13 failure cases were classified as intolerable so additional SOPs were required for each case. Finally, the results of this study provide new insights for stakeholders to revise the rules regarding periodic vehicle testing.

Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck’s condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively.