LAUSR

Accurate and complete traffic information plays an important role in traffic monitoring, traffic state estimation and prediction in all intelligent transportation systems. However, in practice, data provided from various types of traffic sensors may suffer from many issues, such as heterogeneity, missing data and bias. Therefore, extracting unbiased, accurate and complete data from multiple traffic data sources, referred to as multi-source traffic data reconstruction, is critical before data can be utilized. There are many existing methods for multi-source traffic data reconstruction, but two issues remain challenging and unaddressed: the effectiveness of reconstruction is fairly limited to attributes of available data, especially data loss ratio and reliability; the probability distribution of data assumed, which brings in bias prior to data analytics. This paper proposes a novel framework that integrates low-rank representation and fundamental traffic flow models into multi-source traffic data reconstruction. It makes full use of multi-source data, and can simultaneously impute missing data and eliminate outliers/errors. Multiple parameters in the traffic flow models are learned from large-scale spatio-temporal data, which ensures the extracted and imputed data are consistent with traffic flow physics. Experimental results on real-world traffic data demonstrate the superiority of this method over existing methods.