Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …

Subway passenger flow prediction is of great significance in transportation planning and
operation. Special events, as for vocal concerts and sports games, lead large-scaled …

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for
traffic prediction. While recent works have shown improved prediction performance by using …

Prediction of microstructure evolution during material processing is essential to control the
material properties. Simulation tools for microstructure evolution prediction based on …

Abstract Machine learning models were developed for the intermittent multi-mode operation
of a wellhead reverse osmosis water purification and desalination system to predict salt …

Recent development in sensing and communication technologies has made the collection of
a large amount of traffic data easy and transportation engineering has entered the big data …

Correlated time series analysis plays an important role in many real-world industries.
Learning an efficient representation of this large-scale data for further downstream tasks is …

This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs),
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …

Tensor clustering has become an important topic, specifically in spatiotemporal modeling,
because of its ability to cluster spatial modes (eg, stations or road segments) and temporal …

Accurate traffic forecasting is vital to intelligent transportation systems, which are widely
adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling …