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Retrieving and analyzing transit feeds relies on working with analyticalworkflows that can handle the massive volume of data streams that are relevantto understand the dynamics of transit networks which are entirely deterministicin the geographical space in which they takes place. In this paper, we considerthe fundamental issues in developing a streaming analytical workflow foranalyzing the continuous arrival of multiple, unbounded transit data feeds forautomatically processing and enriching them with additional informationcontaining higher level concepts accordingly to a particular mobility context.This workflow consists of three tasks: (1) stream data retrieval for creatingtime windows; (2) data cleaning for handling missing data, overlap data orredundant data; and (3) data contextualization for computing actual arrival anddeparture times as well as the stops and moves during a bus trip, and alsoperforming mobility context computation. The workflow was implemented in aHadoop cloud ecosystem using data streams from the CODIAC Transit System of thecity of Moncton, NB. The Map() function of MapReduce is used to retrieve andbundle data streams into numerous clusters which are subsequently handled in aparallel manner by the Reduce() function in order to execute the datacontextualization step. The results validate the need for cloud computing forachieving high performance and scalability, however, due to the delay incomputing and networking, it is clear that data cleaning tasks should not onlybe deployed using a cloud environment, paving the way to combine it with fogcomputing in the near future.

The design of a streaming analytical workflow for processing massive transit feeds