  Wireless trajectory data consists of a number of (time, point) entries where
each point is associated with a particular wireless device (WAP or BLE beacon)
tied to a location identifier, such as a place name. A trajectory relates to a
particular mobile device. Such data can be clustered `semantically' to identify
similar trajectories, where similarity relates to non-geographic
characteristics such as the type of location visited. Here we present a new
approach to semantic trajectory clustering for such data. The approach is
applicable to interpreting data that does not contain geographical coordinates,
and thus contributes to the current literature on semantic trajectory
clustering. The literature does not appear to provide such an approach, instead
focusing on trajectory data where latitude and longitude data is available.
  We apply the techniques developed above in the context of the Onward Journey
Planner Application, with the motivation of providing on-line recommendations
for onward journey options in a context-specific manner. The trajectories
analysed indicate commute patterns on the London Underground. Points are only
recorded for communication with WAP and BLE beacons within the rail network.
This context presents additional challenge since the trajectories are
`truncated', with no true origin and destination details.
  In the above context we find that there are a range of travel patterns in the
data, without the existence of distinct clusters. Suggestions are made
concerning how to approach the problem of provision of on-line recommendations
with such a data set. Thoughts concerning the related problem of prediction of
journey route and destination are also provided.
