The direction of this research has recently attracted scientists from diverse disciplines, being
not only a major intellectual challenge, but also given its importance in domains such as
urban planning, sustainable mobility, transportation engineering, public health, and economic
forecasting. The European FET project GeoPKDD (Geographic Privacy-aware Knowledge Discovery
and Delivery, www.geopkdd.eu, 2005-2009) is a precursor in mining human mobility data,
which has developed various analytical and mining methods for spatio-temporal data. This and
other projects, in Europe and internationally, have shown how to support the complex knowledge
discovery process from the raw data of individual trajectories up to high-level collective
mobility knowledge, capable of supporting the decisions of mobility and transportation managers,
thus revealing the striking analytical power of big mobility data. Analysts reason about
these high-level concepts, such as systematic versus occasional movement behaviour, purpose
of a trip, and home-work commuting patterns. Accordingly, the mainstream analytical tools of
transportation engineering, such as origin/destination matrices, are based on semantically rich
data collected by means of eld surveys and interviews. It is therefore not obvious that big,
yet raw, mobility data can be used to overcome the limits of surveys, namely their high cost,
infrequent periodicity, quick obsolescence, incompleteness, and inaccuracy.