An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.