Introducing the Rail Accident Analysis Model and Algorithm by Using Neuro-Fuzzy Intelligent Systems (Case Study Derailment)

Due to the safety of Rail transportation, Investigation on characteristics andphenomenon which causes accidents has an important role, in order to enhance thesafety of rail transport and, in turn, reduce the rate of accidents.According to the rail accident statistics, among all factors causing train accidents,derailment has the biggest share, which can be because of human errors, track androlling stock deficiencies, etc.Investigation and technical analyses of these factors will be complicated, because ofthe quantitative/qualitative relationships.Investigation on available derailment results indicates that a few numbers oftraditional analytical models have been used. Herein, limited capability of thesemodels such as being one dimensional, taking not into account all factors involvedand complexity in behavior description can be clearly observed. Regarding theimportance of the current situation in the derailment investigation, in this research anew combined intelligent Neuro-Fuzzy model has been developed.This study has simplified the quantitative/qualitative behavior description forderailment, prepared and simplified a practical and more accurate quantitative/qualitative evaluation to achieve multi-dimensional decision making evaluationmodel, by using Adaptive Neuro Fuzzy Inference System (ANFIS). This research hasdeveloped a method of operation based on human factors. Regarding the results,analyses of the developed N-F model make a foundation for a more comprehensiveaccident management system.