Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Abstract

Among the electromechanical components of the rail system, the rail system vehicle is one of the most important units that carrying the passenger load. In terms of the efficiency of the signalization system, it is very critical to create the optimum vehicle driving profile. While many parameters of the vehicle come into play while designing the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. With the developing technology in rail transportation systems, the use of programmable devices and software instead of human factors is becoming more widespread day by day. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially if driverless (GOA4) signaling systems are preferred, these software become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been carried out by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.