Abstract
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.
Highlights
Restoration and Prediction MethodUrban big data involves the daily life of citizens and the stable running of industries; it is characterized by large volume, complex sources, and heterogeneous structure [1].How to make full use of urban big data to analyze city development issues and provide informational assistance for government departments has attracted great interest in recent years [2,3]
The experimental results show that recovering the missing data is helpful in improving the prediction accuracy
Univariate spline and CP decomposition, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD)
Summary
Urban big data involves the daily life of citizens and the stable running of industries; it is characterized by large volume, complex sources, and heterogeneous structure [1]. To take full advantage of multi-mode characteristics, and recover missing data as much as possible, some researchers have applied tensor decomposition to traffic flow prediction [27,28]. We use a high-dimensional tensor to represent the traffic flow data, considering “temporal-spatial-periodic” multi-mode characteristics. Univariate spline and CP decomposition, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD). We apply CP decomposition for the residual value tensor, and add “day-hour trend” features and the “hour-minute trend” features after reconstruction, which can recover missing data better; We propose the Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS).
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