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 …

Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the
analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can …

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 …

Recently, low-rank tensor completion (LRTC) methods by exploiting the global low-rankness
of the target tensor have shown great potential for traffic data imputation. However, in real …

In recent years, the imputation of spatiotemporal traffic data has emerged as a critical area of
research within intelligent transportation systems. A commonly employed approach is low …

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 …

Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-
driven analysis. While prior works have demonstrated the promise of tensor completion …

Performing accurate and efficient traffic data repair has become an essential task before
proceeding with other applications of intelligent transportation systems. However, existing …

Traffic speed is central to characterizing the fluidity of the road network. Many transportation
applications rely on it, such as real-time navigation, dynamic route planning, and congestion …

Traffic missing data imputation is a fundamental demand and crucial application for real-
world intelligent transportation systems. The wide imputation methods in different missing …