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Chairs(s):

Xiao Li, Texas A&M Transportation Institute

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Description:

Transportation and social science research are undergoing a paradigm shift due to the emergence of big data. With the maturing of data acquisition and transmission techniques, unprecedented amounts of geographic data are continually being generated and collected from various data sources, such as social media, street-view imageries, road-side sensors, cellular signaling data, GPS-enabled smartphones, and connected & autonomous vehicles (CAVs), marking the evolution of the geospatial research paradigm into a new era of geoinformatics. Compared to conventional data sources, these emerging urban-geo datasets are massive in size, spatiotemporally fine-scaled, and high dimensional (e.g., multivariate and multivalued), providing researchers with a rich source of information to gain new insights into transportation safety, congestion, planning, management, among others. Although these emerging data sources are promising, they usually show different data characteristics and therefore lead to different applications and limitations, which need to be discussed and documented. Meanwhile, managing and analyzing these big complex datasets also expose new problems and challenges, commonly described as the “big data tsunami” and categorized into four types, namely (1) technological, (2) methodological, (3) social & political, and (4) theoretical and epistemological.

Non-Presenting Participants Agenda