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通过知识图增强的生成对抗网络来计算流量数据

Yinghui Liu¹, Guojiang Shen¹, Nali Liu¹

¹College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.

PeerJ. Computer science

|December 9, 2024

概括

本研究介绍了一个知识图增强的生成对抗网络 (KG-GAN),用于改进流量数据归算. 通过整合外部因素,KG-GAN有效地处理高缺失数据率,提高智能运输系统 (ITS) 的可靠性.

科学领域:

智能运输系统 (ITS) 是一种智能运输系统.
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

交通数据的归算对ITS至关重要,但当前的深度学习方法因高缺失数据率和有限的外部因素集成而陷入困境.
挑战包括在不完整的交通信息下保持系统可靠性和效率.

研究的目的:

开发一种新的深度学习模型,用于准确的交通数据归算,解决现有方法的局限性.
通过结合与外部流量相关的知识和改进时空依赖模型来提高归算性能.

主要方法:

提出一个以知识图增强的生成对抗网络 (KG-GAN) 用于流量数据的归算.
构建一个精细的知识图 (KG) 来表示外部因素,如POI和天气.
引入一个知识意识嵌入单元 (EM-cell) 来整合外部知识与空间时间GAN输入的流量数据.

主要成果:

在各种缺失数据场景中,KG-GAN显著优于交通数据归算的最先进方法.
废弃性研究证实了将外部知识纳入实质性性能增长.
该模型在处理不完整的交通数据集时表现出更好的准确性和稳定性.

结论:

拟议的KG-GAN有效地解决了交通数据归算的挑战,特别是在高缺失数据率的情况下.
通过精细的KG整合外部知识可以显著提高ITS应用程序的归算质量.
这种方法为可靠的交通预测和管理系统提供了更强大的解决方案.

关键词:

生成性的对抗性网络.知识图表知识图表交通数据归算流量数据.

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