Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …

Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …

Traffic forecasting has attracted considerable attention due to its importance in proactive
urban traffic control and management. Scholars and engineers have exerted considerable …

With the increasing demand for intelligent transportation systems, short-term traffic flow
prediction has become an important research direction. The memory unit of a Long Short …

For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …

PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …

Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …