Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Next Point-of Interest (POI) recommendation plays an important role in location-based
applications, which aims to recommend the next POIs to users that they are most likely to …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

Spatial temporal forecasting plays an important role in improving the quality and
performance of Intelligent Transportation Systems. This task is rather challenging due to the …

The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Traffic flow forecasting plays a vital role in the transportation domain. Existing studies usually
manually construct correlation graphs and design sophisticated models for learning spatial …

Computing trajectory similarities is a critical and fundamental task for various spatial-
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …

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 …

Deep learning based trajectory similarity computation holds the potential for improved
efficiency and adaptability over traditional similarity computation. However, existing learning …

The computation of trajectory similarity is a crucial task in many spatial data analysis
applications. However, existing methods have been designed primarily for trajectories in …