Trajectory clustering has played an essential role in trajectory mining tasks. It serves in a
wide range of real-life applications, including transportation, location-based services …

The problem of detecting co-movement patterns from trajectories has been extensively
studied by data science community. Existing methods are based on filter-and-refine …

Subtrajectory query has been a fundamental operator in mobility data management and
useful in the applications of trajectory clustering, co-movement pattern mining and contact …

With the explosive ubiquity of GPS-equipped devices, eg, mobile phones, vehicles, and
vessels, a massive amount of real-time, unbounded, and varying-sampling trajectory …

Trajectory clustering is an essential task in spatial data mining. To address this problem,
many previous studies either extended traditional clustering algorithms with spatial features …

Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method,
has been proposed to mine frequent relative orders in a time series. Although frequent …

Predictive analytics over mobility data is of great importance since it can assist an analyst to
predict events, such as collisions, encounters, traffic jams, etc. A typical example is …

Abstract Analysis on spatial-temporal data has several benefits that range from an improved
traffic network in a city to increased earnings for drivers and ridesharing companies. A …