A traffic flow data imputation method based on a rank-adaptive Bayesian tensor decomposition model
HAO Wei
LIU Fang
WANG Xiaolu
ZHANG Zhaolei
XU Hanmeng
TANG Jinjun
Abstract：Transmission line failures and communication breakdowns in intelligent transportation sys-tems can result in data loss due to the inability to detect vehicles at specific times or intervals. To ad-dress this issue,this paper proposes a traffic flow data imputation method based on a rank-adaptive Bayesian tensor decomposition model. First,considering the spatiotemporal correlations in traffic data,the proposed method constructs data structures using a tensor model. Then,a Bayesian approach is employed to set flexible priors and hyperpriors on the parameters and hyperparameters of the tensor decomposition,and a rank-adaptive algorithm is developed to address the rank selection problem. Finally,the method is validated using traffic flow data recorded by the License Plate Recog-nition (LPR) system from 793 intersections in Changsha between July 1 and July 28,2019. This study examines the model's imputation accuracy under various tensor data structures,data loss patterns,loss rates,and tensor decomposition ranks. The results indicate that the rank-adaptive algorithm effec-tively captures the optimal rank for tensor decomposition,avoiding overfitting due to an excessively high preset rank. Compared to traditional CANDECOMP/PARAFAC decomposition and mean impu-tation methods,the proposed algorithm reduces the mean absolute percentage error by 20％ even with a 30％ data loss rate,significantly enhancing the accuracy of traffic flow data imputation. These find-ings provide valuable insights for data imputation in traffic flow prediction and spatiotemporal traffic pattern analysis.
Keywords：intelligent transportationdata imputationrank-adaptive Bayesian tensor decomposition modelLPR data
Publication Date：2024-08-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 82-92 )