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Chairs(s):

Xiao Li, Texas A&M Transportation Institute

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Description:

The study of human mobility is of great importance for a wide range of applications, such as traffic flow estimation, travel demand change, urban planning, epidemic modeling, accessibility & equity, among others. Current human mobility research is undergoing a paradigm shift due to the emergence of big data. With the advancement of data acquisition and transmission techniques, unprecedented amounts of mobility data are continually being generated and collected from various data sources, such as social media, road-side sensors, cellular signaling data, GPS-enabled smartphones, and connected & autonomous vehicles (CAVs). Compared to conventional data sources, these emerging mobility 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 human mobility and mobility-related transportation and social challenges (e.g., traffic congestion, inequity of accessibility, segregation, etc.) However, managing and analyzing these big complex datasets expose new problems and challenges in terms of strategies to promote multiple aspects, including (1) data integration, enrichment, storage, archiving, and sharing, (2) data quality control (e.g., reduce data uncertainty and redundancy), (3) data security, integrity, and privacy, and (4) data processing and visualization.

Non-Presenting Participants Agenda