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  4. Vol 10, Issue 1, 2026

Logistics Behavior Analysis and Predictive Modeling Methods Based on Multimodal Data Fusion

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DOI: 10.23977/cpcs.2026.100103 | Downloads: 3 | Views: 124

Author(s)

Xuanmin Che 1

Affiliation(s)

1 R&T Logistics Inc, 4882W 145th St Hawthorne, CA, 90250, America

Corresponding Author

Xuanmin Che

ABSTRACT

This review paper provides a comprehensive overview of logistics behavior analysis and predictive modeling methods based on multimodal data fusion. The increasing complexity of modern logistics necessitates advanced analytical techniques to optimize operations, enhance efficiency, and improve decision-making. Multimodal data fusion, which integrates diverse data sources such as GPS tracking, sensor data, transaction records, and weather information, offers a powerful approach to understanding and predicting logistics behavior. This paper explores the historical evolution of logistics analytics, focusing on the shift from traditional statistical methods to contemporary machine learning techniques. Core themes examined include data acquisition and preprocessing, feature engineering for logistics applications, and the application of various predictive models such as regression models, time series analysis, and deep learning algorithms. A comparative analysis highlights the strengths and weaknesses of different modeling approaches, addressing challenges related to data heterogeneity, scalability, and real-time processing. Furthermore, the review identifies emerging trends and future research directions in multimodal logistics data analysis, including the integration of blockchain technology, the utilization of edge computing, and the development of explainable AI models. This paper aims to serve as a valuable resource for researchers and practitioners seeking to leverage multimodal data fusion for improved logistics management, offering insights into the state-of-the-art techniques and future opportunities in this rapidly evolving field.

KEYWORDS

Logistics; Multimodal Data Fusion; Predictive Modeling; Machine Learning; Data Analytics; Optimization, Supply Chain

CITE THIS PAPER

Xuanmin Che. Logistics Behavior Analysis and Predictive Modeling Methods Based on Multimodal Data Fusion. Computing, Performance and Communication Systems (2026). Vol. 10, No. 1, 17-25. DOI: http://dx.doi.org/10.23977/cpcs.2026.100103.

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