Spatial-Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition

被引:8
|
作者
Yan, Jiahe [1 ]
Li, Honghui [1 ]
Bai, Yanhui [1 ]
Lin, Yingli [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 19期
基金
国家重点研发计划;
关键词
urban big data; traffic flow prediction; tensor CP decomposition; INTELLIGENT TRANSPORTATION SYSTEMS;
D O I
10.3390/app11199220
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering "temporal-spatial-periodic " characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.
引用
收藏
页数:14
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