Service optimization of transnational logistics transportation based on big data analysis

Chufeng Yan

doi:10.1117/12.3036707

21 July 2024 Service optimization of transnational logistics transportation based on big data analysis

Chufeng Yan

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132194A (2024) https://doi.org/10.1117/12.3036707
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

Logistics plays an important role in modern society, and the ability to precisely predict the package arrival time is also crucial for improving customer satisfaction and loyalty. To greater optimize transnational logistic transportation service, this paper researches the factors that effects on arrival time and how to accurately estimate the delivery time of an order. This paper utilizes multiple linear regression to reveal the relationship between each variable and the arrival time, analyzes all the factors in detail, and accordingly use five different methods of machine learning, choose the best to build a logistics prediction system to predict the arrival time of package. Subsequently, this paper leverages the significant variables identified in the multiple linear regression analysis to offer logistics companies guidance on directions for future enhancements. In terms of model construction, XGBoost performs best with the existing dataset, and the prediction model constructed based on this machine learning method has excellent prediction accuracy. Through accurate logistics forecasting systems and multiple regression analysis, companies can increase customer satisfaction and loyalty, improve their service levels, and take responsive measures to minimize arrival times.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chufeng Yan "Service optimization of transnational logistics transportation based on big data analysis", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132194A (21 July 2024); https://doi.org/10.1117/12.3036707

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