A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data
A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data
复制标题
DOI：
10.3390/math12020223
复制
发表时间：
2024-01
期刊：
影响因子：
2.4
通讯作者：
Yongmei Zhao
中科院分区：
文献类型：
--
作者：
Yongmei Zhao
完善交通数据是智能交通系统的基本要求。然而，完成时空交通数据提出了一个重大的挑战，特别是对于高维数据具有复杂的缺失机制。针对不同缺失机制的各种补全方法通过有效地表征复杂的时空相关性展示了张量学习的优越性。在这项研究中，提出了一种新的张量完成框架，称为多源张量数据融合完成方法。该框架将公交与地铁之间的换乘关系纳入地铁数据补全中，提高了数据补全的准确性。此外，通过将公交换乘客流数据与不同路段、时间间隔、天数等数据维度相结合，得到了创新的四维低秩张量补全数据框架。此外，为了提高补全精度，推导了截断l2，p范数优化模型。该模型增强了整个张量完成过程中目标函数的非凸性能。结果突出了所提出的完成方法的优越性，利用融合的地铁/公交数据比其他完全依赖于地铁数据的完成方法。
Completing traffic data is a basic requirement for intelligent transportation systems. However, completing spatiotemporal traffic data poses a significant challenge, especially for high-dimensional data with complex missing mechanisms. Various completion methods targeting different missing mechanisms have showcased the superiority of tensor learning by effectively characterizing intricate spatiotemporal correlations. In this study, a novel tensor completion framework, known as the multi-source tensor completion method for data fusion, is proposed. This framework incorporates passenger transfer relationships between buses and subways into subway data completion, enhancing the data completion accuracy. Moreover, by combining bus transfer passenger flow data with other data dimensions, such as the different road sections, time intervals, and days, an innovative 4D low-rank tensor completion data framework was obtained. In addition, to boost the completion accuracy, a truncated l2,p norm optimization model was derived. This model enhances the non-convex performance of the objective function throughout the tensor completion process. The results highlight the superiority of the proposed completion method, leveraging fused subway/bus data over other completion methods that rely solely on subway data.