Efficient transportation planning and management are critical for ensuring the smooth
operation of rail transit systems, particularly in urban areas with high passenger demand …

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

In this study, we address the limitations of current deep learning models in road extraction
tasks from remote sensing imagery. We introduce MixerNet-SAGA, a novel deep learning …

There is growing pressure to manage and run urban rail transit networks as more people
select this mode of transportation for their travel. It is, therefore, essential to create a precise …

Origin–destination (OD) flow prediction is crucial for predicting inter-station passenger flows
in intelligent transport systems. However, previous OD prediction methods have ignored the …

For sophisticated management, advertisement placement, and epidemic prevention control
of urban rail transit (URT), accurate and real-time predictions of passenger flows at different …

Short-term origin-destination (OD) matrix prediction in metro systems faces challenges of
high dimensionality, data sparsity, incomplete information, and semantic complexity. This …

Short-term origin-destination (OD) demand predicting plays an indispensable role in
intelligent transportation systems and ride-hailing service operations. However, most studies …

Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving
URT operation. However, due to the problems such as the unavailability of the OD matrix of …

With the increasing demand for traffic management and resource allocation in Intelligent
Transportation Systems (ITS), accurate origin-destination (OD) prediction has become …