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

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Chen:2024:asoc,

• author = "Xinan Chen and Rong Qu and Jing Dong and Haibo Dong and Ruibin Bai",
• title = "Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments",
• journal = "Applied Soft Computing",
• year = "2024",
• volume = "166",
• pages = "112190",
• keywords = "genetic algorithms, genetic programming, Intelligent intersection, Transport simulation, Reinforcement learning, Port optimization",
• ISSN = "1568-4946",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494624009645",
• DOI = "doi:10.1016/j.asoc.2024.112190",
• 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 35percent to about 7percent, 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 characterised by sparse data",

}

Genetic Programming entries for Xinan Chen Rong Qu Jing Dong Haibo Dong Ruibin Bai

Citations