preprints.org > engineering > transportation science and technology > doi: 10.20944/preprints202309.0696.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 8 September 2023 / Approved: 11 September 2023 / Online: 12 September 2023 (02:50:20 CEST)

Online and offline shopping trip have different impacts on various aspects of urban life, such as e-commerce, transportation systems, and sustainability. Therefore, it is important to evaluate the factors that influence their choices. We use a hybrid machine learning model that combines a gray wolf optimization algorithm and a deep convolutional neural network to estimate shopping trip based on a survey of 1,000 active e-commerce users who made successful orders in both online and offline services in the last 20 days of 2021 in areas 2 and 5 of Tehran. The gray wolf optimization algorithm performs feature selection and hyperparameter tuning for the deep convolutional neural network, which is a powerful deep learning model for image recognition and classification. The results show that 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, 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%, 90.0%, 86.49%, and 80.16%, respectively.

online shopping trip; offline shopping trips; gray wolf optimization; deep neural network model; e-commerce and transportation; factors affecting shopping trip choice; sustainable development

Engineering, Transportation Science and Technology

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