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 …

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

Individual passenger travel patterns have significant value in understanding passenger's
behavior, such as learning the hidden clusters of locations, time, and passengers. The …

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 …

This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM), a
framework to infer the passenger distribution inside the whole urban rail transit (URT) …

Trajectory recovery based on the snapshots from the city-wide multi-camera network
facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions …

Passenger clustering based on travel records is essential for transportation operators.
However, existing methods cannot easily cluster the passengers due to the hierarchical …