  Retrieving and analyzing transit feeds relies on working with analytical
workflows that can handle the massive volume of data streams that are relevant
to understand the dynamics of transit networks which are entirely deterministic
in the geographical space in which they takes place. In this paper, we consider
the fundamental issues in developing a streaming analytical workflow for
analyzing the continuous arrival of multiple, unbounded transit data feeds for
automatically processing and enriching them with additional information
containing higher level concepts accordingly to a particular mobility context.
This workflow consists of three tasks: (1) stream data retrieval for creating
time windows; (2) data cleaning for handling missing data, overlap data or
redundant data; and (3) data contextualization for computing actual arrival and
departure times as well as the stops and moves during a bus trip, and also
performing mobility context computation. The workflow was implemented in a
Hadoop cloud ecosystem using data streams from the CODIAC Transit System of the
city of Moncton, NB. The Map() function of MapReduce is used to retrieve and
bundle data streams into numerous clusters which are subsequently handled in a
parallel manner by the Reduce() function in order to execute the data
contextualization step. The results validate the need for cloud computing for
achieving high performance and scalability, however, due to the delay in
computing and networking, it is clear that data cleaning tasks should not only
be deployed using a cloud environment, paving the way to combine it with fog
computing in the near future.
