PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING.

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams thanks to their smaller size and maneuverability, therefore, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values were also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions were close to the actual values. Also, the high R-squared-Coefficient of Determination (R²) value (0.947) suggested that this model explained the data quite well, and its predictions approached the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation. [ABSTRACT FROM AUTHOR]