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 are important during the design of the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. The use of programmable devices and software instead of human factors is becoming more and more widespread day by day with the developed technology in rail transportation systems. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially, in the driverless (GOA4) signaling systems, these softwares become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been done by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.