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Introduction to this special issue: urban analytics and mobility (part 2)

Published: 13 November 2018 Publication History

Abstract

According to a US Census report [2], the daytime population of cities like Washington D.C. nearly doubles the nighttime population, coining the notion of "Mega Commuting". To understand, explain, and predict urban mobility, our current data-centered era provides a plethora of rich data sources. These data sources capture mobility on the road, including GPS trajectories, metro, bus and taxi origin-destination data, indoor navigation data and many more types and sources of data.

References

[1]
D. Schrank, B. Eisele, T. Lomax, and J. Bak. Urban Mobility Scorecard. The Texas A&M Transportation Institute and INRIX, 2015.
[2]
U.S. Census Bureau. U.S. Department of Commerce. Economics and Statistics Administration. Measuring America: An Overview to Commuting and Related Statistics https://www.census.gov/content/dam/Census/data/training-workshops/recorded-webinars/commuting-nov2014.pdf.

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Published In

cover image SIGSPATIAL Special
SIGSPATIAL Special  Volume 10, Issue 2
July 2018
40 pages
EISSN:1946-7729
DOI:10.1145/3292390
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2018
Published in SIGSPATIAL Volume 10, Issue 2

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