ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1705687
Game-Theoretic SHAP-Driven Interpretable Forecasting of Air Cargo Demand Using Bayesian-Optimized Random Forests
Provisionally accepted- 1Jiangsu Aviation Technical Colledge, Zhenjiang, China
- 2Jiangsu University of Science and Technology, Zhenjiang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Reliable forecasting of air cargo demand is crucial for optimizing logistics operations, scheduling air freight capacity, and reducing operational costs in a dynamic global supply chain environment. This study proposes a novel interpretable forecasting framework that integrates Bayesian-optimized Random Forests (BO-RF) with game-theoretic SHAP (SHapley Additive exPlanations) analysis to enhance both prediction accuracy and model transparency. The proposed BO-RF model leverages Bayesian Optimization to fine-tune hyperparameters efficiently, thus improving the generalization performance of Random Forests on small-sample air cargo datasets. To address the interpretability challenge of machine learning models, SHAP values are introduced, providing theoretically grounded, fair attribution of each input feature's marginal contribution based on cooperative game theory. Experiments based on real-world monthly air cargo data demonstrate that the proposed method outperforms traditional machine learning benchmarks in both accuracy and interpretability. By combining Bayesian-optimized ensemble learning with SHAP-based interpretability, the study contributes to the growing literature on explainable, data-driven forecasting in transportation and provides actionable insights for demand management and capacity planning in the air freight industry.
Keywords: Air cargo demand forecasting, Bayesian optimization, random forest, SHAP values, Game theory, Explainable machinelearning
Received: 15 Sep 2025; Accepted: 09 Oct 2025.
Copyright: © 2025 Zhang and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Liang Jiang, jiangliang@just.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.