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A new form of deep learning in smart logistics with IoT environment

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Abstract

The purpose of this study is to introduce a method to fill the gap in urban freight volume prediction in the Internet of Things by utilising deep learning for meeting smart logistics requirements in China. To meet this objective, researchers reviewed relevant literature to demonstrate that deep learning is appropriate for logistics use. Chinese urban transportation system has been selected as the study object with 9 years of data to examine the deep learning application in Chinese urban transportation in the IoT environment. Researchers introduce the deep learning method to predict urban road freight volume and design the prediction model architectures of two new DL algorithms. Model training and parameter adjustment are also tricky in the research of this article. It is necessary to understand the role of each parameter in the algorithm and flexibly use the relevant DL framework in Python to obtain an ideal model through multi-fold cross-validation and multiple trials. The final results show that the transportation freight volume prediction in the Internet of Things by utilising deep learning has excellent prediction effects to meet smart logistics requirements compared with traditional methods.

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Acknowledgements

The paper supported by 2021 Guangxi Education Science ‘14th Five Years’ Plan 2021 Special Project “Development and Practice of Innovation and Entrepreneurship Course System of Guangxi Higher Vocational colleges from the perspective of Integration of Industry and Education” (2021ZJY1460); Meanwhile,2018 Guangxi Philosophy and Social Science Planning Office Project: Research on the Dynamic Mechanism and Model Innovation of Guangxi Logistics Enterprises' Cross-Border Integration and Growth (18BGL010); 2020 Guangxi Vocational Education and Teaching Reform Key Research Project supports this paper and Teaching Reform Key Research Project "Under the background of health and wealth planning, the four-in-one mixed teaching research and practice of 'class match and post certificate' for financial majors". (GXGZJG2020A012); This paper is also supported by Special for the key field of college and universities in Guangdong Province. (2021zdzx1092) ;Dongguan Science and Technology of Social Development Program in 2020 (2020507156694), Special fund for science and technology innovation strategy of Guangdong Province in 2021 (special fund for climbing plan) (pdjh2021a0944), Special projects in key fields of colleges and universities in Guangdong Province in 2021(2021ZDZX1093), Dongguan Science and Technology of Social Development Program in 2021 (20211800900252); Project of Humanities and social sciences of “cultivation plan for thousands of young and middle-aged backbone teachers in Guangxi Colleges and universities” in 2021: Research on Collaborative integration of logistics service supply chain under high-quality development goals (2021QGRW044)

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Authors and Affiliations

  1. School of Modern Circulation, Guang Xi International Business Vocational College, Nanning, China

    Fei Jiang

  2. Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning, China

    Xiao-Ya Ma

  3. School of Economics and Finance, Guang Xi International Business Vocational College, Nanning, China

    Yan-Hua Zhang

  4. International Business Department, Shangdong Vocational College of Economics and Business, Weifang, China

    Li Wang

  5. Department of Computer Engineering, Dongguan Polytechnic, Dongguan, China

    Wen-Liang Cao & Jian-Xin Li

  6. Nanning Normal University, Nanning, China

    Jin Tong

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  1. Fei Jiang
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Jiang, F., Ma, XY., Zhang, YH. et al. A new form of deep learning in smart logistics with IoT environment. J Supercomput 78, 11873–11894 (2022). https://doi.org/10.1007/s11227-022-04343-4

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