Clustering Method in Road Safety Index Forecasting using Intelligent Nonlinear Approximators

This paper presents Intelligent Hybrid methods for forecasting the Road SafetyIndex that is based on a clustering method. The Road Safety Index is one of themost important problems in transportation, which is connected to road accidentsdirectly. This is a vital problem because it's related to the health and economy ofpeople seriously. Locations of road that these accidents occurred are a hugethreat to the people's lives, so we should predict these places to prevent ordiminish these accidents. Due to the fluctuation and nonlinearity of Road SafetyIndex, we should give an effective method to predict these accidents perfectly.The objective is to predict Safety Road Index accurately based on proposedhybrid models which combine Fuzzy-Cmeans, Artificial Neural Networks andAdaptive Neuro-Fuzzy Inference System. The significant advantages of thisapproach include higher accuracy, lower error value and more correlationcoefficient. The results are calculated by the training and testing the proposedmethodologies. The experimental results illustrate that these hybrid methodshave a better response in comparison with the other conventional neural networkmodels and neuro-fuzzy systems.

کلیدواژه ها:

Fuzzy-Cmeans ، Artificial Neural Networks ، Radial Basis Function Neural Networks ، Adaptive Neuro-Fuzzy Inference System ، Hybrid Intelligent Methods ، Road Safety Index