Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …

In this paper, we study the problem of large-scale trajectory data clustering, k-paths, which
aims to efficiently identify k" representative" paths in a road network. Unlike traditional …

The ubiquity of mobile devices and the accompanying deployment of sensing technologies
have resulted in a massive amount of trajectory data. One important fundamental task is …

The ability to collect large numbers of trajectory data through GPS-enabled devices have
enabled a myriad of very important applications that are widely used on a daily basis. This …

Similar trajectory search is a fundamental problem and has been well studied over the past
two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return …

The process of computing a representative trajectory for a set of raw (or even semantically
enriched) trajectories is an attractive solution to minimize several challenges related to …

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 …

Spatio-temporal mobility patterns are at the core of strategic applications such as urban
planning and monitoring. Depending on the strength of spatio-temporal constraints, different …

Trajectory clustering is an important operation of knowledge discovery from mobility data.
Especially nowadays, the need for performing advanced analytic operations over massively …

Joining trajectory datasets is a significant operation in mobility data analytics and the
cornerstone of various methods that aim to extract knowledge out of them. In the era of Big …