Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

Abstract

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

Comparative Analysis of the Predictive Performance of an ANN and Logistic Regression for the Acceptability of Eco-Mobility Using the Belgrade Data Set

“…When it comes to microvehicles, research focuses on the spatiotemporal demand for micromobility vehicle services (e.g., refs. [34,35]) both shared and personal, then classification of electric scooters and scooters with the help of smartphones [36], the estimation of the time and cost of the services provided by electric scooters [37], and so on. Bearing in mind that the spread of the application of both prediction models is wide, indicating their good predictive power, this paper aimed to reveal the most significant findings regarding the use of electric-powered microvehicles by applying both models.…”

“…However, when it comes to micromobility, numerous works refer to the spatiotemporal demand for micromobility vehicle services (e.g., refs. [34,35]) both shared and personal, the classification of electric scooters and scooters with the help of smartphones [36], the estimation of the time and cost of the services provided by electric scooters [37], and similar.…”

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

“…Gradient Boosting (GB) is a powerful ML technique for solving classification and regression problems. Its basic idea is to build a robust predictive model by sequentially combining weak learners (usually, decision trees are used) [33]. The main principle of GB is that each new tree is trained by focusing on the mistakes of previous trees.…”

Neural Network and Random Forest Algorithms for Estimation of the Waiting Times Based on the DES in ED

“…Margins of error should be low in ML algorithms. However, the higher the coefficient of determination (R 2 ), which is the accuracy coefficient, the more accurate the validity of the estimated values obtained in the ML algorithms [21].…”