The rapid increase of the population and the number of motor vehicles brought about the transportation problem today. It has brought the efforts of the operators to determine the headway of the vehicles during the day in order to minimize the waiting times of the passengers at the stops and increase the satisfaction of the passengers, taking into account the passenger demand. Nowadays, especially during the current pandemic period (COVID-19), passenger demand forecasting becomes much more significant, so that measures can be taken and headway planning can be made to adjust the social distance by identifying the number of passengers in advance. In this study, the significance of demand forecasting in the railway sector is considered, and the study tackles the issue in two stages: on line and station basis that make the study different from others. In the first stage of the study, passenger demand forecasting is made on line basis with statistical techniques such as regression analysis and simple average, the mean absolute percentage error values are calculated and compared. Regression analysis is conducted with SPSS Statistics 21.0 programme. In the second stage of the study, passenger demand forecasting is made with artificial neural network and machine learning (ML) algorithms technique on station basis and the error values (mean absolute error, BIAS, mean squared error, mean absolute percentage error, and root mean squared error) are compared. As a result of the study, while the best demand forecasting method is simple average on line basis, it is seen that the most successful and reliable results for demand forecasting on station basis are obtained through decision tree, which is one of the ML algorithms.
The significance and novelty of the present work is the preparation of non-lead ceramics with the general formula of (1 − x)K0.5Na0.5NbO3–xLaMn0.5Ni0.5O3 (KNN–LMN) with different values of x (0 < x < 20) (mol%) to examine the shielding qualities of the KNN–LMN ceramics. This is done by carrying out Phy-X/PSD calculation and predicting the attenuation behavior of the samples by utilizing the deep learning (DL) algorithm. From the attained results, it is seen that the higher the x (concentration of LMN in the KNN–LMN lead-free ceramics), the better the shielding proficiency observed in terms of gamma-shielding performance for the chosen KNN–LMN-based lead-free ceramics. In all sections, good agreement is observed between Phy-X/PSD results and DL predictions.
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