Decision Support Model for Automated Railway Level Crossing System Using Fuzzy Logic Control

Abstract

This paper aims to discuss a decision support model for automated railway level crossing (LC) using fuzzy logic control (FLC) for providing robust decision making at unmanned railway level crossings, to save the overall operation time, to avoid any accidental fatalities, and to eliminate human errors. The decision support model proposed here provides intelligent decisive action signals as similar to a human brain (e.g. during arrival and departure of trains at railway level crossing). FLC model is designed which recognizes the events (i.e. arrival and departure of trains) and accordingly output action signals are generated (i.e. to warning siren, control actions for opening and closing of gates). This type of model can be implemented in unmanned railway level where the chances of accidents are higher and reliable control operation is required. Three primary inputs to the specified model are considered based on visual, acoustic, and vibration. This novel system makes use of all these three parameters as input for its decision taking parameters, which increases the robustness of this model as compared with previously proposed models where the input is dependent on a single event. The FLC structure implemented to generate this model is multiple input multiple output (MIMO) system.