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Title: Spatiotemporal Regularized Factorization for Traffic Data Imputation Authors:  Aurelie Labbe - HEC Montreal (Canada) [presenting]
Abstract: The traffic system is a system of spatiotemporal distributions. Spatiotemporal traffic data, which can beregarded as multivariate time series, are often collected using a network of sensors. However, sensor technologies or any other data collection methods are not flawless, as factors ranging from technology malfunction to human error can cause incompleteness in data. Hence, the missing data problem in the traffic field is often unavoidable. This can be a hindrance to the the performance of data-driven intelligent transportation system (ITS) applications, and other subsequent traffic prediction tasks. Thus, it is essential to develop a reliable imputation method that can help recover missing data as accurately as possible. In this paper, we propose a framework for incorporating the spatial correlation of road network topology, and the temporal dependencies of time series by building a spatiotemporal regularized factorization model to impute for missing traffic data. Specifically, we use weighted Laplace matrix and temporal spline graph as a smoothing approach for retaining and finding the global structure similarity in the spatial and temporal dimensions, respectively. We examine the effectiveness of the proposed spatiotemporal regularized factorization model on a traffic volume data set. The result shows the model that is spatiotemporal regularized, achieved the best imputation accuracy compared to the model that is not spatiotemporal regularized.