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 …

Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …

Since high-quality real data acquired from selected road sections are not always available, a
traffic control solution can use data from software traffic simulators working offline. The …

Spectrum prediction is a promising technique for improving spectrum exploitation in
cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the …

Reliable and accurate traffic flow prediction is crucial for the construction and operation of
smart highways, supporting scientific traffic management and planning. However, accurately …

Real-world traffic prediction is challenging and requires accuracy, efficiency, and
generalizability for applications. Most studies used two-step data imputation-prediction …

This paper presents the first neural process-based model for traffic prediction, the Memory-
augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction …

A networked time series (NETS) is a family of time series on a given graph, one for each
node. It has a wide range of applications from intelligent transportation to environment …

In practice, time series data obtained is usually small and missing, which poses a great
challenge to data analysis in different domains, such as increasing the bias of model …

The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety.
Therefore, proactively predicting LC maneuver and associated collision risk is of paramount …