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 flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

With the continuous evolution and refinement of urban functional spaces, the escalating
reliance of commuters on public transportation for work-related travel has surged with time …

Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic
flow forecasting can provide an effective reference for implementing traffic management …

The urban metro system accommodates significant travel demand and alleviates traffic
congestion. Improving metro operational efficiency can increase the metro operator revenue …

It is critical for the management and control of urban rail transit (URT) to be able to predict
passenger flow accurately and in real time. Considering that the high-resolution data …

In the case of missing data, traffic forecasting becomes challenging. Many existing studies
on traffic flow forecasting with missing data often overlook the relationship between data …

Rational use of urban underground space (UUS) and public transportation transfer
underground can solve urban traffic problems. Accurate short-term prediction of passenger …

Origin-destination (OD) short-term passenger flow forecasting (OD STPFF) in urban rail
transit (URT) is essential for developing timely network measures. The capacity utilization …