A New Approach for Rule Estimation of Fuzzy Inference System: A Case Study for Public Transport Maintenance System

Abstract

The increase in the population and the high amount of individual vehicle usage in the big cities brought traffic congestion and environmental problems. Additionally, these issues have also some negative effects on the public transport systems (PTSs). In this respect, the analysis of PTS is critical and important for both city life and people. It is possible that the failures in PTS can lead to many problems. Disruption of daily life, loss of lives and property or damage to the environment are only just a few of these problems. In this context, effective maintenance planning for PTSs is so crucial. In this study, the rule estimation for a fuzzy rule-based system (FRBS) which takes into consideration many factors for the maintenance planning of PTSs is discussed. The rule-based system for maintenance planning of Bus Rapid Transit System (BRT) will be highly effective for the prediction of failures for PTSs and the correct actions to be taken. Rule estimation for this system is aimed to increase the precision and flexibility of maintenance procedures. In this context, a model based on artificial neural networks (ANNs) has been developed and used in rule estimation for FRBS. For this aim, ten cases that are not in the rule base system are estimated and the results of the fuzzy rule-based maintenance inference system for the relevant inputs are revealed. Thus, it has been shown that ANNs can be used effectively for the analysis of rules that are not included in the current rule-based maintenance system.