Fusing Incomplete Multisensor Heterogeneous Data to Estimate Urban Traffic

Today, data-driven intelligent transportation systems must address data quality challenges, such as the missing data problem. For example, is it possible to improve the performance of traffic state estimation using incomplete data? In this article, an incomplete traffic data fusing method is proposed to estimate traffic state accurately. It improves missing data estimation by extracting data correlations and applying incomplete data fusion, implementing the two approaches in parallel. The main research focus is on extracting the inherent spatio-temporal correlations of traffic states data from road segments based on a multiple linear regression (MLR) model. Computational experiments for accuracy and efficiency demonstrate that this method can use data correlations to implement accurate and real-time traffic state estimation. This article is part of a special issue on quality modeling.