Abstract: In the face of the current huge amount of intelligent traffic data,collecting and statistical processing is a necessary and important process,but the problem of inevitable data missing is the current research focus.Aiming at the problem of vehicle traffic data missing,this paper proposed a new method based on tensor for vehicle traffic data missing estimation,Integrated Bayesian tensor decomposition (IBTD).In the data model construction stage,the random sampling principle was used to randomly extract the missing data to generate a subset of data,and the optimized Bayesian tensor decomposition algorithm was used for interpolation.By introducing the integration idea,the error results after multiple interpolations were analyzed and sorted,consider the spatio-temporal complexity,and choose the optimal average to get the best result.The performance of the proposed model was evalua-ted by mean absolute percentage error (MAPE) and root mean square error (RMSE).Experimental results show that the proposed method can effectively interpolate the traffic datasets with different missing quantities and get good interpolation results.

Key words: Bayesian tensor decomposition, Data missing, Random sampling, Tensor, Traffic data