Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS).
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …

Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Spatio-temporal graphs are important structures to describe urban sensory data, eg, traffic
speed and air quality. Predicting over spatio-temporal graphs enables many essential …

Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …