Missing data is a pervasive issue in both scientific and engineering tasks, especially for the
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …

Intelligent Transportation Systems (ITS) are essential and play a key role in improving road
safety, reducing congestion, optimizing traffic flow and facilitating the development of smart …

Sensing network-wide traffic information is fundamental for the sustainable development of
urban planning and traffic management. However, owing to the limited budgets or device …

Accurate monitoring and sensing network-wide traffic conditions under uncertainty is vital for
addressing urban transportation obstacles and promoting the evolution of intelligent …

Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive
advanced techniques have been designed to capture these structures for effective …

Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the
multiscale transportation system. Existing methods aim to reconstruct STTD using low …

Modeling and forecasting multivariate time series not only facilitates the decision making of
practitioners, but also deepens our scientific understanding of the underlying dynamical …

This paper focuses on the multivariate time series imputation problem using deep neural
architectures. The ubiquitous issue of missing data in both scientific and engineering tasks …

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due
to limited sensor deployment and potential data corruption. In this study, we propose an …

High-quality spatial–temporal traffic data is crucial for the functioning of modern smart
transportation systems. However, the collection and storage of traffic data in real-world …