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

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 …

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 …

Graph-based methods have witnessed tremendous success in traffic prediction, largely
attributed to their superior ability in capturing and modeling spatial dependencies. However …

Long-separated research has been conducted on two highly correlated tracks: traffic and
incidents. Traffic track witnesses complicating deep learning models, eg, to push the …

Multi-task learning is a machine learning paradigm, that aims to leverage useful domain
information to help improve the generalization performance of all tasks. Learning the …

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

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV
navigation face challenges in generalization, particularly when applied to unseen complex …

Traditional server failure prediction methods predominantly rely on single-modality data
such as system logs or system status curves. This reliance may lead to an incomplete …

Trajectory representation learning (TRL) maps trajectories to vectors that can be used for
many downstream tasks. Existing TRL methods use either grid trajectories, capturing …