Discovering flow patterns across spatial and temporal scales

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We are witnessing a paradigm shift in gathering mobility data at scales that were not possible before. In particular, vehicle mobility data has been collected using a range of technologies such as high spatial resolution satellites, traffic detection video cameras, satellite-enabled Global Positioning System (GPS) and mobile phone networks. Although technology unveils a "centimetres everywhere anytime" scenario in the near future, recent research has demonstrated that vehicle mobility data being generated today suffers from limited population sizes and short experimental duration due to the use of a single data source. Flow pattern discovery brings additional challenges on how to handle cross-scale effects when using vehicles mobility data sets from different sources (e.g. vehicle mounted GPS and traffic cameras).This research aims to develop a scale-free geographic knowledge process for uncovering the formation, the structure and the dynamics of flow patterns. Flow patterns are inherently not random as they have a scale-free structure in space (e.g. from vehicles, crossings, highways, to networks) as well as time (e.g. from minutes to an hour time). New mobility measurement and flow pattern techniques will be developed to mitigate the scale issues due to the use of different data sources. The successful outcome of this research will also contribute to narrow the knowledge gap between the vastly collected vehicle mobility data and very limited capacity of flow pattern information extracted from this data today. It will help users to effectively utilize the discovered flow patterns within their applications such as Intelligent Transportation Systems, Smart Cities of the future, and large-scale Emergency Evacuation Planning. The education of high quality researchers and engineers in the proposed research field is strategically important for keeping Canada's leading position in geomatics.