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- W4206709881 abstract "While determining the travel time between stations, a number of design parameters such as waiting time, motion resistance, slope, curve, traction force, maximum speed, vehicle mass, and distance between two stations are taken into consideration. These parameters form the infrastructure of the system definition of the motion of the vehicle. Furthermore, while creating the speed profile, special attention should be paid to the travel time in order to ensure the defined headway for the line. In this study, the travel time value between stations for intracity metro stations was predicted using the adaptive boosting method, which is one of the machine learning methods, and compared with various well-known methods. The data used were applied to the proposed model with the cross-validation and random sampling hold-out methods, and the values of the coefficient of determination (R2) were calculated." @default.
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- W4206709881 date "2022-01-05" @default.
- W4206709881 modified "2023-10-02" @default.
- W4206709881 title "Prediction of travel time for railway traffic management by using the AdaBoost algorithm" @default.
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- W4206709881 doi "https://doi.org/10.25092/baunfbed.937333" @default.
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