Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Abstract

Transportation systems take an essential place in traffic planning. While designing railways, transportation between access points is planned to be realized within the desired time. The average speed between access points is affected by parameters like waiting time, motion resistance, slope, curve, traction force, maximum speed, the mass of the vehicle, and distance between two stations. The motion of the vehicle is calculated with these parameters, and the system design is performed accordingly. The average speed is one of the most critical factors affecting travel time between two access points. The headway may vary depending on the average speed. In this study, different regression methods were applied to estimate the average speeds calculated between stations in rail systems, and the obtained successful results were presented comparatively. When the methods used were examined, the Gaussian process regression method, which was optimized with the Bayesian algorithm, was observed to yield the most successful result. A Gaussian process (GP) is a collection of random variables, any finite number of which have a Gaussian distribution. Following simulations, the root mean square error and mean absolute error were found to be 0.064 and 0.047, respectively, and the coefficient of determination (R2) value was obtained as 1 when the success rate of the method was calculated.