Missing traffic data repairing method based on Bayesian enhanced tensor

Abstract

The invention discloses a missing traffic data repairing method based on Bayesian enhanced tensor decomposition. The method comprises the following steps of organizing the road network vehicle speed data into a third-order data tensor to introduce a dominant factor structure for modeling; inputting the data tensor and indicating the tensor; updating posterior distribution of a global parameter mu;updating posterior distribution of a hyper-parameter; updating posterior distribution of a bias parameter phi and posterior distribution of a factor matrix parameter U until i is equal to m; updatingposterior distribution of a bias parameter theta and posterior distribution of a factor matrix parameter V until j is equal to n; updating posterior distribution of a bias parameter eta and posteriordistribution of a factor matrix parameter X until t is equal to f; repeating the steps S5-S9 until the difference deltatau between the accuracy parameter tau and the previous parameter tau is less than epsilon, converging the model and proceeding to the next step; substituting the updated {mu, phi, theta, eta, U, V, X} parameter values into the expressions of yijt to calculate and estimate the tensor

Description

technical field [0001] The invention relates to the technical field of intelligent traffic systems, and more specifically, to a method for repairing missing traffic data based on Bayesian enhanced tensors. Background technique [0002] Missing data has become a common and unavoidable problem in the field of intelligent transportation systems, and the reasons for this problem are various. First of all, due to the natural sparsity of some traffic data, it cannot be effectively collected completely. In addition, the limited spatial distribution of sensors limits the completeness of data from the perspective of traffic management costs. Furthermore, uncertainty factors such as communication failure and transmission failure of the data acquisition equipment itself are another common factor. Therefore, it is necessary to accurately repair missing data and enhance data quality to support the application of intelligent transportation systems. [0003] Taking road speed as an exam...

AI Technical Summary

Problems solved by technology

Existing traffic data filling techniques based on tensor decomposition cannot simultaneously mine explicit and implicit traffic features, resulting in inaccurate data estimation and poor model interpretation

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