Analysis and Prediction of Small and Medium-sized Express Logistics Data of Nanning Jingdong Based on Big Data

Yunsheng Chen; Mingyang Liu; Mengdie Wu; Dalong Liu

doi:10.2991/978-94-6239-598-5_20

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Analysis and Prediction of Small and Medium-sized Express Logistics Data of Nanning Jingdong Based on Big Data

Authors

Yunsheng Chen¹, Mingyang Liu¹, Mengdie Wu¹, Dalong Liu¹^{, *}

¹College of Traffic and Transportation, Nanning University, Nanning, Guangxi, China

^*Corresponding author. Email: liudalong@unn.edu.cn

Corresponding Author

Dalong Liu

Available Online 26 February 2026.

DOI: 10.2991/978-94-6239-598-5_20 How to use a DOI?
Keywords: logistics market flow; Big data; SPSS; Predictive analysis; Computer modeling
Abstract: With the rapid development of the global economy and the Internet industry, the logistics industry is facing both opportunities and challenges. This study leverages big data processing and computer modeling techniques to analyze and predict the logistics volume of JD.com’s small and medium-sized express delivery in Nanning. We employ SPSS for statistical modeling. The methodology includes data cleaning, standardization, correlation analysis, and multiple regression modeling (linear, Deming, hierarchical, and ridge regression). The results demonstrate a strong positive correlation between order quantity and supply chain transportation costs (r = 0.947, P = 0.001), with the linear regression model achieving a high fit (R² = 0.896) and low prediction error (MAPE = 0.48%). The study provides a computer-aided decision support framework for logistics cost management and operational planning, highlighting the practical value of integrating statistical software with scripting tools for logistics data analysis.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2025 6th International Conference on Big Data and Social Sciences (ICBDSS 2025)
Series: Advances in Computer Science Research
Publication Date: 26 February 2026
ISBN: 978-94-6239-598-5
ISSN: 2352-538X
DOI: 10.2991/978-94-6239-598-5_20 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yunsheng Chen
AU  - Mingyang Liu
AU  - Mengdie Wu
AU  - Dalong Liu
PY  - 2026
DA  - 2026/02/26
TI  - Analysis and Prediction of Small and Medium-sized Express Logistics Data of Nanning Jingdong Based on Big Data
BT  - Proceedings of the 2025 6th  International Conference on Big Data and Social Sciences (ICBDSS 2025)
PB  - Atlantis Press
SP  - 192
EP  - 202
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-598-5_20
DO  - 10.2991/978-94-6239-598-5_20
ID  - Chen2026
ER  -

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