HighlightsA tensor completion algorithm is proposed for imputing multidimensional traffic data.The algorithm iteratively estimates a parsimonious Tucker model of an imputed tensor.The parsimonious Tucker model is obtained by soft thresholding the core of an HOSVD.The relevance of the parsimony criterion is discussed and illustrated with real data.Simulations with real data show our method outperforms four other tensor-based ones.
AbstractTechnological limitations and practical difficulties cause inevitable losses of traffic data in the typical processing chain of an intelligent transportation system. This has motivated the development of imputation algorithms for mitigating the consequences of such losses. As the involved datasets are usually multidimensional and bear strong spatio-temporal correlations, we propose for traffic data imputation a tensor completion algorithm which promotes parsimony of an estimated orthogonal Tucker model by iteratively softly thresholding its core. The motivation of this strategy is discussed on the basis of characteristics typically possessed by real-world datasets. An evaluation of the proposed method using speed data from the Grenoble south ring (France) shows that our algorithm outperforms other imputation methods, including tensor completion algorithms, and delivers good results even when the loss is severely systematic, being mostly concentrated in long time windows (of up to three hours) spread along the considered time horizon.
Traffic data imputation via tensor completion based on soft thresholding of Tucker core
Tramsportation Research, Part C: Emerging Technologies ; 85 ; 348-362
2017-09-12
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch