多式联运运输系统的共同短期原点-目的地需求预测
IEEE transactions on pattern analysis and machine intelligence
|October 30, 2025
概括
准确的短期多式联运运输需求预测是具有挑战性的,因为数据限制和多式联运的影响. 拟议的PD-MTSOD模型通过分析时空特征和模式间相关性,有效地预测出发目的地需求.
科学领域:
- 运输科学 运输科学
- 数据科学数据科学数据科学
- 应用数学 应用数学 应用数学
背景情况:
- 短期原点-目的地 (OD) 需求预测对于多式联运运输系统至关重要.
- 现有的方法在实时数据可用性,需求稀疏性,高维度和多式联运影响方面扎.
研究的目的:
- 为准确的短期多式联运运输OD需求预测开发一种新型模型.
- 为了应对数据可用性,稀疏性,高维度和模式间相关性等挑战.
主要方法:
- 提出了一种多任务学习和基于部分差异的模型 (PD-MTSOD).
- 整合了一个OD需求学习器,用于实时需求估计.
- 利用了超图的注意力来进行时空特征聚合.
- 分解了OD需求,并采用了部分微分方法来建模模式之间的相关性.
主要成果:
- 与基线模型相比,PD-MTSOD在对北京和纽约市多式联络系统的测试中表现出卓越的性能.
- 验证了共同考虑多种运输方式的好处.
- 揭示了不同运输方式之间的显著相关性.
结论:
- PD-MTSOD模型为短期多式联运运输OD需求预测提供了一种可靠的方法.
- 联合分析多种运输方式可以提高预测的准确性.
- 了解模式间的相关性对于有效的运输管理至关重要.
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