集成图形神经网络和LSTM用于智能端口多模式系统中的路径优化
Jiangjiang He1, Weixun Chen2, Jiaren Sun1
1School of Transportation and Logistics, Guangzhou Railway Polytechnic, Guangzhou, China.
PloS one
|December 2, 2025
概括
本研究介绍了GL-SSL模型用于智能港口多式联运运输路径优化,显著减少路径长度,成本和延误. 该模型在动态环境中表现出色,提供强大高效的路径规划解决方案.
科学领域:
- 海上物流的海上物流
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 由于动态环境和复杂的数据融合,智能港口在优化多式联运方面面临着挑战.
- 现有的方法难以整合多种数据源,并适应实时网络变化.
研究的目的:
- 为智能港口中多式联运运输路径优化提出一个先进的模型.
- 通过有效地融合多式联网数据和利用网络拓,提高路径规划效率.
主要方法:
- 开发了GL-SSL模型,集成图形神经网络 (GCN),长短期记忆 (LSTM) 和自主监督学习 (SSL).
- 利用AIS数据,全球航运数据和港口收入数据进行模型培训和验证.
- 利用图形结构的港口网络和时间变化来优化路径规划.
主要成果:
- 实现了80公里的优化路径长度,并将运输成本降低到200个成本单位.
- 保持0.05 (5%) 的低延迟率,表现优于传统和其他深度学习模型.
- 在高峰交通和恶劣天气等复杂场景中表现出稳定的性能和稳定性.
结论:
- GL-SSL模型为智能端口的多式联运运输路径优化提供了显著的改进.
- 该模型显示出强大的适应性,稳定性和在动态港口环境中的实际应用潜力.
- 这项研究提供了有效的技术支持,对智能港口物流具有理论意义.
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