Abstract: The problem of missing data seriously affects a series of activities in intelligent transportation systems,such as monitoring traffic dynamics,predicting traffic flow,and deploying traffic planning through data.Therefore,a traffic flow data reconstruction model WLRTC-TTNN(low rank tensor completion of weighted and truncated nuclear norm)combined with weighted and truncated nuclear norm is proposed by using the low-rank tensor completion framework based on tensor singular value decomposition,which can effectively repair the missing spatio-temporal traffic data.The truncated nuclear norm of the tensor is used as a convex proxy for tensor rank minimization instead of tensor rank minimization,which preserves the main feature information inside the spatio-temporal traffic data,and further optimizes the model by penalizing smaller singular values according to the gene-ralized singular value threshold theory,and finally the WLRTC-TTNN algorithm is implemented using the alternating multiplier method.Experiments are conducted on two publicly available spatio-temporal traffic datasets selected with different missing scenarios and missing rates,and the results show that the complementary performance of WLRTC-TTNNN is better than that of other baseline models,and the overall complementary accuracy improves by 3%~37%,and the complementary effect is more stable in extreme missing scenarios.

Key words: Intelligent transportation, Data repair, Tensor weighting, Truncated kernel norm, Alternating multiplier method