{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:32:38Z","timestamp":1750221158873,"version":"3.41.0"},"reference-count":2,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGSPATIAL Special"],"published-print":{"date-parts":[[2018,11,13]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3292390.3292392","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T13:23:31Z","timestamp":1542201811000},"page":"2-2","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Introduction to this special issue"],"prefix":"10.1145","volume":"10","author":[{"given":"Andreas","family":"Z\u00fcfle","sequence":"first","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Urban Mobility Scorecard. The Texas A&M Transportation Institute and INRIX","author":"Schrank D.","year":"2015","unstructured":"D. Schrank , B. Eisele , T. Lomax , and J. Bak . Urban Mobility Scorecard. The Texas A&M Transportation Institute and INRIX , 2015 . D. Schrank, B. Eisele, T. Lomax, and J. Bak. Urban Mobility Scorecard. The Texas A&M Transportation Institute and INRIX, 2015."},{"key":"e_1_2_1_2_1","unstructured":"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.  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."}],"container-title":["SIGSPATIAL Special"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292390.3292392","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292390.3292392","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:08:38Z","timestamp":1750208918000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292390.3292392"}},"subtitle":["urban analytics and mobility (part 2)"],"short-title":[],"issued":{"date-parts":[[2018,11,13]]},"references-count":2,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,11,13]]}},"alternative-id":["10.1145\/3292390.3292392"],"URL":"https:\/\/doi.org\/10.1145\/3292390.3292392","relation":{},"ISSN":["1946-7729"],"issn-type":[{"type":"electronic","value":"1946-7729"}],"subject":[],"published":{"date-parts":[[2018,11,13]]},"assertion":[{"value":"2018-11-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}