Document Type : Original Article
Authors
1
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Professor, Department of Mathematical Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
10.22034/tri.2023.412304.3179
Abstract
Online and offline shopping trips affect various aspects of urban life, such as e-commerce, transportation systems, and sustainability. To evaluate the factors that influence shoppers' choices, we propose a hybrid machine learning model that combines a gray wolf optimization algorithm and a deep convolutional neural network. We apply this model to estimate shopping trips based on a survey of 1000 active e-commerce users in districts 2 and 5 of Tehran, who made successful orders in both online and offline services in 2020. The gray wolf optimization algorithm performs feature selection and hyperparameters tuning for the deep convolutional neural network, which is a powerful deep learning model for image recognition and classification. Our model achieves an accuracy of 97.81% with an MSE of 0.325 by selecting seven out of ten features. The most important features are delivery cost, delivery time, product price, and car ownership. In addition, comparing the performance of the proposed method with other methods showed that the proposed algorithm with an accuracy of 97.81%, the accuracies of the single deep learning model, MLP neural network, decision tree, and KNN models were 95.63%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. It aims to help them design effective strategies to reduce transportation costs, lower pollutant emissions, alleviate urban traffic congestion, and enhance user satisfaction all while promoting sustainable development.
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