A tensor-based method for missing traffic data completion

Missing and suspicious traffic data are inevitable due to detector and communication malfunctions, which adversely affect the transportation management system (TMS). In this paper, a tensor pattern which is an extension of matrix is introduced into modeling the traffic data for the first time, which can give full play to traffic spatial–temporal information and preserve the multi-way nature of traffic data. To estimate the missing value, a tensor decomposition based Imputation method has been developed. This approach not only inherits the advantages of imputation methods based on matrix pattern for estimating missing points, but also well mines the multi-dimensional inherent correlation of traffic data. Experiments demonstrate that the proposed method achieves a better imputation performance than the state-of-the-art imputation approach even when the missing ratio is up to 90%. Furthermore, the experimental results show that the proposed method can address the extreme case where the data of one or several days are completely missing, and additionally it can be employed to recover the missing traffic data in adverse weather as well.

► The tensor pattern is introduced to model traffic data for the first time.
► A Tucker decomposition based imputation method (TDI) is proposed to impute the missing traffic volume.
► Experiments on real traffic data show the proposed imputing method performance better than other methods.
► TDI can address some extreme cases including missing several days data and non-recurrent traffic.