Advanced Modeling in Civil Engineering
Application of the Fuzzy Delphi Method in Proactive Identification of Hazardous Road Locations Based on Perceived Safety (Case Study: Kerman-Gonabad Road)
Document Type : Original Article
Authors
1 Assistant Professor Department of Civil Engineering, Faculty of Engineering, University of Gonabad, Gonabad, Iran
2 Bachelor's degree Urban Engineering Dept., College of Architecture and Urbanism, Ferdowsi University of Mashhad. Mashhad, Iran.
Abstract
Traffic crashes on two-lane rural roads present a critical challenge to transportation safety, traditionally evaluated through retrospective crash data. To address this limitation, the present study aims to proactively identify Hazardous Road Locations by evaluating subjective safety and experts’ risk perception, and to compare these findings with the objective safety of the roadway. To achieve this, the Fuzzy Delphi Method (FDM) was employed as a robust and structured tool. For validation, a case study on the Kerman-Gonabad route involved 19 experts assessing driver-perspective images. By utilizing triangular fuzzy numbers (l, m, u), FDM effectively modeled the uncertainties inherent in visual and linguistic judgments, successfully achieving expert consensus in two rounds.
The findings demonstrated that the outputs of this novel analytical tool are completely consistent with the statistical analyses from previous studies. Specifically, “horizontal curves with inadequate sight distance” (Z=0.88), “vertical curves with inadequate sight distance” (Z=0.81), and “sharp horizontal curves” (Z=0.80) were classified by the experts as “completely unsafe,” highlighting the critical role of sight distance limitations in elevating perceived risk. Conversely, a standard road section was accurately categorized as “safe” (Z=0.27), as expected. The most significant contribution of this research is demonstrating the high efficacy and potential of the Fuzzy Delphi Method as an innovative preventive tool. It enables the precise screening of high-risk locations even in the absence of historical crash data, thereby opening new horizons for proactive road safety management.
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