Özet:

Transportation systems have an important place in traffic planning. When transport systems are taken into account, railways cover a large share of the entire system. When the railways are designed, the transport between access points is planned to be carried out in the desired time. The average speed between access points is affected by parameters such as waiting time, movement resistance, curve, curve, cer strength, maximum speed, the mass of the vehicle and the distance between the two stations. The movement of the tool is calculated by these parameters and the system design is performed according to this. The average speed is one of the most important elements affecting the travel time between two access points. Depending on average speed, the frequency time can change. In this study, different regression methods were applied to estimate the average speed calculated between stations in rail systems and the achieved successful results were comparable. The study of the methods used has shown that the Gaussian Process Regression method optimized by the Bayesian algorithm has given the most successful results. The Gauss process (GP) is a collection of random variables with any final number of Gauss distribution. After comparisons, the root mean square error and the mean absolute error were found as 0.064 and 0.047 respectively, and the determination rate (R2) was obtained as 1 when the method's success rate was calculated.

Anahtar Kelimeler:

Özet:

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.