Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments

Chen, Xinan; Qu, Rong; Dong, Jing; Dong, Haibo; Bai, Ruibin

Abstract

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35% to about 7%, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterized by sparse data.

Citation

Chen, X., Qu, R., Dong, J., Dong, H., & Bai, R. (2024). Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments. Applied Soft Computing, 166, Article 112190. https://doi.org/10.1016/j.asoc.2024.112190

• Journal Article Type: Article
• Acceptance Date: Aug 30, 2024
• Online Publication Date: Sep 1, 2024
• Publication Date: 2024-11
• Deposit Date: Sep 20, 2024
• Publicly Available Date: Sep 2, 2025
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 166
• Article Number: 112190
• DOI: https://doi.org/10.1016/j.asoc.2024.112190
• Public URL: https://nottingham-repository.worktribe.com/output/39177969
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1568494624009645?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments; Journal Title: Applied Soft Computing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.asoc.2024.112190; Content Type: article; Copyright: © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.