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

Traffic prediction is crucial for transportation management and user convenience. With the
rapid development of deep learning techniques, numerous models have emerged for traffic …

Congestion on road networks has a negative impact on sustainability in many cities through
the exacerbation of air pollution. Smart congestion management allows road users to avoid …

Accurate traffic prediction is critical for enhancing the performance of intelligent
transportation systems. The key challenge to this task is how to properly model the complex …

Data-driven models, especially deep learning models, are proposed for remaining useful life
(RUL) estimation with multisensor signals. Various treatments to reduce data sensitivity …

Traffic forecasting is a very challenging task due to the complicated and dynamic spatial–
temporal correlations between traffic nodes. Most existing methods measure the spatial …

Traffic forecasting is a particularly challenging and important application direction in the field
of spatial–temporal prediction. However, it is difficult for existing models to accurately …

Environmental time series modeling of recirculating aquaculture systems provides the basis
for the design of intelligent and foreseeable agricultural facilities. The modeling accuracy of …

Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically
represent critical elements/communication links that play a key role in a system's …

Movement analytics and mobility insights play a crucial role in urban planning and
transportation management. The plethora of mobility data sources, such as GPS trajectories …