Journal of Transportation Research

Journal of Transportation Research

Predict the Shopping Trips (Online and Offline) using a combination of a Gray Wolf Optimization Algorithm (GWO) and a Deep Convolutional Neural Network (Case Study: Tehran)

Document Type : Original Article

Authors
MohammadHanif Dasoomi 1
Ali Naderan 2
Tofigh Allahviranloo 3
1 Ph.D., candidate, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 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.
Keywords
Online Shopping Trips
Offline Shopping Trips
Gray Wolf Optimization Deep Neural Network Model
Sustainable Development

  • Annual Report of Urban Traffic and Transportation Organization in Tehran City. (2020). Available online: https://www.ictte.ir/data/cnf1668321135/uploads/amar/1399.pdf(in Persian)

    -Archetti, C., & Bertazzi, L. (2021). Recent challenges in Routing and Inventory Routing: E-commerce and last-mile delivery. Networks, 77(2), 255-268. doi.org/10.1002/net.21995

    Ardiansah, M. N., Chariri, A., & Januarti, I. (2019). Empirical study on customer perception of e-commerce: Mediating effect of electronic payment security. Jurnal Dinamika Akuntansi, 11(2), 122-131. org/10.15294/jda.v11i2.20147

    • Bansal, A., & Srivastava, P. (2021). Factors affecting consumer buying behavior of online travel agencies. Elementary Education Online, 2021 May 10;20(1):2958
    • Candra, S., Nita, S., Loang, OK., Basmantra, IN., & Wong, NK. (2022). Consumer Buying Behavior in Online Travel Agent: A Preliminary Finding. In 2022 International Conference on Information Management and Technology (ICIMTech), 2022 Aug 11 24-27.
    • https://10.0.4.85/ICIMTech55957.2022.9915179
    • Collins, LM. (2007). Research Design and Methods. In Encyclopedia of Gerontology; Elsevier: Amsterdam, the Netherlands, Vol. 2, 419–429.doi.org/10.1016/B0-12-370870-2/00162-1
    • Dasoomi, MH., Naderan, A., & Allahviranloo, T. (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, 15(20), 14764. org/10.3390/su152014764
    • Dong, Y., Tang, J., & Zhang, Z. (2022). Integrated Machine Learning Approaches for E-commerce Customer Behavior Prediction. In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022), 1008-1015. Atlantis Press.doi.org/10.2991/aebmr.k.220307.166
    • Dou, Z., Sun, Y., Zhu, J., & Zhou, Z. (2023). The Evaluation Prediction System for Urban Advanced Manufacturing Development, Systems, 11(8), 392.doi.org/10.3390/systems11080392
    • Hussien, AG., Houssein, EH., & Hassanien, AE. (2017). A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection. In 2017 Eighth international conference on intelligent computing and information systems (ICICIS), 2017 Dec 5, IEEE. 166-172.doi.org/10.1109/INTELCIS.2017.8260031
    • Jawandhiya, P. (2018). Hardware design for machine learning. International Journal of Artificial Intelligence and Applications (IJAIA), 9(1), 63-84.dx.doi.org/10.5121/ijaia.2018.9105
    • Jeffers, J., & Reinders, J. (2015). High performance parallelism pearls volume two: multicore and many-core programming approaches. Morgan Kaufmann.
    • Jiang, H., He, M., Xi, Y., & Zeng, J. (2021). Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services. Information, 12(5), 180. org/10.3390/info12050180
    • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. org/10.1126/science.aaa8415
    • Kamel, SR., YaghoubZadeh, R., & Kheirabadi, M. (2019). Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer. Journal of Big Data, 6(1):90.
    • Kennedy, I. (2022). Sample Size Determination in Test-Retest and Cronbach Alpha Reliability Estimates. Br. J. Contemp. Educ. 2022, 2, 17–29.doi.org/10.52589/BJCE-FY266HK9
    • Khrais, LT. (2020). Role of Artificial Intelligence in Shopping Consumer Demand in E-Commerce. Future Internet, 12(12), 226. org/10.3390/fi12120226
    • Li, C., Wang, Y., Lv, X., & Li, H. (2021). To buy or not to buy? The effect of time scarcity and travel experience on tourists' impulse buying. Annals of Tourism Research, 86, 103083.doi.org/10.1016/j.annals.2020.103083
    • Lipsman, June 27, (2019). Global ecommerce 2019. https://www.emarketer.com/content/global-ecommerce.
    • Memeti, S., Pllana, S., Binotto, A., Kołodziej, J., & Brandic, I. (2019). Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing, 101(8), 893-936.doi.org/10.1007/s00607-018-0614-9
    • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. doi.org/10.1016/j.advengsoft.2013.12.007
    • Moon, J., Choe, Y., & Song, H. (2021). Determinants of consumers’ online/offline shopping behaviors during the COVID-19 pandemic. org/10.3390/ijerph18041593
    • Moshayedi, A.J., Roy, A.S., Taravet, A.;,Liao, L., Wu, J., Gheisari, M. (2023).
      A secure traffic police remote sensing approach via a deep learning-based low-altitude vehicle speed detector through uavs in smart cites: Algorithm, implementation and evaluation. Future Transp, 3, 189–209.
    • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.doi.org/10.1007/s42979-021-00592-x
    • Shao, R., Derudder, B., & Witlox, F. (2022). The geography of e-shopping in China: On the role of physical and virtual accessibility. Journal of Retailing and Consumer Services, 64, 102753.doi.org/10.1016/j.jretconser.2021.102753
    • Stocchi, L., Michaelidou, N., Pourazad, N., & Micevski, M. (2018). The rules of engagement: How to motivate consumers to engage with branded mobile apps. Journal of Marketing Management, 34(13-14),
      1196-1226. org/10.1080/0267257X.2018.1544167
    • Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 1-32. org/10.1186/s40537-015-0030-3
    • Xiahou, X., & Harada, Y. (2022). B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475. org/10.3390/jtaer17020024