Improved Hierarchical Fuzzy TOPSIS for Road Safety Performance Evaluation

Abstract

With the ever increasing public awareness of complicated road safety phenomenon, much more detailed aspects of crash and injury causation rather than only crash data are extensively investigated in the current road safety research. Safety performance indicators (SPIs), which are causally related to the number of crashes or to the injury consequences of a crash, are rapidly developed and increasingly used. To measure the multi-dimensional concept of road safety which cannot be captured by a single indicator, the exploration of a composite road safety performance index is vital for rational decision-making about road safety. In doing so, a proper decision support system is required. In this study, we propose an improved hierarchical fuzzy TOPSIS model to combine the multilayer SPIs into one overall index by incorporating experts’ knowledge. Using the number of road fatalities per million inhabitants as a relevant reference, the proposed model provides with a promising intelligent decision support system to evaluate the road safety performance for a case study of a given set of European countries. It effectively handles experts’ linguistic expressions and takes the layered hierarchy of the indicators into account. The comparison results with those from the original hierarchical fuzzy TOPSIS model further verify the robustness of the proposed model, and imply the feasibility of applying this model to a great number of performance evaluation and decision making activities in other wide ranging fields as well.