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Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran

Author

Listed:
  • Mohammadhanif Dasoomi

    (Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran)

  • Ali Naderan

    (Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran)

  • Tofigh Allahviranloo

    (Department of Mathematical Sciences, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
    Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Turkey)

Abstract

This study examines the determinants of online and offline shopping trip choices and their implications for urban transportation, the environment, and the economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1000 active e-commerce users who successfully placed orders through both online and offline services in districts 2 and 5 of Tehran during the last 20 days of 2021. A deep neural network model was applied to predict the type of shopping trips based on 10 variables including age, gender, car ownership, delivery cost, and product price. The model’s performance was evaluated against four other algorithms: MLP, decision tree, LSTM, and KNN. The results demonstrated that the deep neural network model achieved the highest accuracy, with a rate of 95.73%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. 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.

Suggested Citation

  • Mohammadhanif Dasoomi & Ali Naderan & Tofigh Allahviranloo, 2023. "Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14764-:d:1257786
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    References listed on IDEAS

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    1. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.
    2. Ying Xiong & Lele Qin, 2022. "The Impact of Artificial Intelligence and Digital Economy Consumer Online Shopping Behavior on Market Changes," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-12, May.
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