A fuzzy multicriteria method for ranking the factors that influence the settlement of Brazilian highway speed limits

Lanzaro, Gabriel Andrade; Andrade, Michelle · 2020 · Crossref

DOI: 10.14295/transportes.v28i3.2067

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Summary

This study addresses the challenge of establishing highway speed limits in Brazil, a process currently hindered by subjectivity and the lack of a clear hierarchy among the numerous influencing factors. While Brazilian regulations and international manuals identify various elements—such as land use, crash data, and operational speed—they do not define the relative importance of these variables. This ambiguity makes it difficult for experts to balance traffic fluidity with crash risk minimization. The authors aim to resolve this by ranking the factors that influence speed limit decisions using a fuzzy multicriteria decision-making method, thereby reducing uncertainty and providing a more coherent basis for expert judgment. The methodology combines fuzzy logic with genetic algorithms to handle the imprecision inherent in expert opinions. The researchers selected 20 variables categorized into general, roadside, road, and traffic characteristics, based on previous studies and regulatory manuals. An online survey was conducted with experts from across Brazil, who rated the influence of each factor using a 5-point Likert scale. These linguistic responses were converted into triangular fuzzy numbers to account for ambiguity. The core of the method involves minimizing a mean squared error equation that compares the weighted influence of factors against a control question regarding the importance of the research itself. A genetic algorithm was employed to optimize the weights, utilizing a population of 200 individuals, 2000 generations, and stochastic uniform selection to ensure convergence. The resulting weights were normalized to ensure they summed to one and fell within the valid interval. The study successfully applied this optimized fuzzy multicriteria method to rank the 20 variables according to their relevance in setting speed limits. The genetic algorithm converged to satisfactory results, producing specific weights for each factor that reflect their relative importance. By transforming subjective expert assessments into quantifiable weights, the method provides a clear hierarchy of factors. This allows decision-makers to prioritize the most critical elements, such as operational speed or roadside occupation, over less significant ones, rather than relying on undefined subjective judgments. The significance of this work lies in its potential to standardize the speed limit establishment process in Brazil and other regions with similar challenges. The derived weights can serve as the foundation for an expert system that automates or guides the decision-making process, ensuring consistency and reducing the impact of individual bias. Furthermore, the proposed methodology demonstrates the effectiveness of combining fuzzy logic with genetic algorithms for multicriteria decision-making in contexts characterized by uncertainty. The authors suggest that this approach is not limited to transportation engineering but can be applied to other fields where complex decisions must be made based on multiple, imprecise criteria.

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• speed choice