## 5 Conclusions  
The postal and package delivery industries are looking for new ways to make the postal or package delivery process faster, safer, and less costly. The postal or parcel delivery industries have agreed on two critical issues: cost and time. The two most important objectives of these sectors are to deliver the packages on time, to gain customer satisfaction, to reduce costs, and to prevent cost wastage. In today's world, traditional distribution vehicles are insufficient to meet the necessary needs in terms of time and distribution costs in complex residential areas. In particular, distribution vehicles provide the energy required for mail or package distribution in oil and gas. These two types of energy have adverse effects in terms of both cost and environmental effects. With today's technologies, they are turning to electric vehicles for private transportation and short-distance logistics services. For low-calibre postal products, the PTT company preferred e-scooters and started using them in different regions of Turkey. This study estimated time and energy costs for various areas by analysing the e-scooter data used in Turkey. In this research, ML algorithms estimate the delivery time of the preferred e-scooter delivery vehicle for mail and package delivery and the energy cost required for distribution. We used our collected data and applied multiple machine-learning methods to determine the most accurate model regarding delivery times and energy costs of preferred e-scooter vehicles for mail and parcel delivery. The GB algorithm showed the best performance among the ML algorithms. The performance criteria values were calculated and shared to verify the validity of the results of the GB algorithm in this study. GB algorithm has the highest R\({}^{2}\) and lowest MSE, RMSE, and MAE values compared to other algorithms. The R\({}^{2}\), MSE, RMSE, and MAE values for the GB algorithm distribution time have 0.845, 149.32, 12.22, and 6.08, respectively. In the same algorithm, R\({}^{2}\), MSE, RMSE, and MAE values for energy costs were calculated as 0.953, 0.001, 0.019, and 0.009, respectively. The \(k\)NN algorithm provided the worst value for the delivery time and energy cost estimation results required using an e-scooter for parcel or mail delivery. The R\({}^{2}\) value of the \(k\)NN algorithm for both variables were calculated as 0.786 and 0.365, respectively. In this study, the average energy cost for the distribution of a package with e-scooter delivery vehicles was calculated as 0.125 TL. In another study, the average energy cost for the package distribution in combivan and motorbike vehicles, which are traditional distribution vehicles, was calculated as 1.73 and 0.61 TL, respectively [45]. It has been determined that the delivery time of a package to short-distance delivery addresses using e-scooter vehicles is 20.21% shorter than other traditional delivery vehicles [45]. In addition, e-scooter vehicles cause fewer emissions than conventional vehicles used for distribution. The scientific novelty of this study lies in the efforts to show that these distribution vehicles are more advantageous than the classical distribution vehicles by using the data of e-scooter vehicles, which are among the micro-mobility vehicles that provide advantages in terms of environment, cost, and energy in mail or package distribution based on ML algorithms. Contrary to other studies, this article provides an analysis of large data types such as environmental, geographic, and demographic of micro-mobility vehicles in different regions, unlike traditional delivery vehicles in mail and parcel delivery. The estimation data from the developed algorithms differ in the time and energy cost of using an e-scooter according to input parameters. With these models we offer, it is crucial to estimate the values that the use of e-scooter provide in different regions and with various factors.