<![CDATA[Traffic accidents involving four-wheeled vehicles constitute a complex problem influenced by multiple factors, including vehicle speed and traffic density. Accurate accident-risk assessment is essential to support preventive and control efforts on road networks. This study aims to design and implement an accident-risk evaluation model based on a Mamdani-type Fuzzy Inference System (FIS) to accommodate uncertainty in decision-making. The research procedure includes identifying input and output variables, constructing membership functions, fuzzification, formulating fuzzy rules, and performing inference and defuzzification. Testing results indicate that, for a combination of 80 km/h speed and 70 vehicles/km density, the centroid defuzzification method yields a risk value of 25.31%. This value falls within the low-to-moderate risk category. These findings suggest that the developed Mamdani FIS model is effective as a methodological approach for accident-risk evaluation.]]>
Copyrights © 2026
