Expressway Traffic Flow Missing Data Repair Method Based on Coupled Matrix-Tensor Factorizations

Abstract

Traffic flow data is the basis of traffic management, planning, control, and other forms of implementation. Once missing, it will directly affect the monitoring and prediction of expressway traffic status. Regarding this, this paper proposes a repair method for the traffic flow missing data of expressway, combined with the idea of coupled matrix-tensor factorizations (CMTF), to couple the auxiliary traffic flow data into the main traffic flow data and to construct the coupling matrix-tensor expression of traffic flow data, and the alternating direction multiplier algorithm is used to realize the repair of missing traffic flow data. Combined with the measured data of expressway traffic flow, the experimental results show that, under different missing data types and missing rates, the proposed method outperforms the methods lacking auxiliary traffic flow data and achieves a good repair effect, especially for high miss data rates.