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However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform \u201cin silico\u201d experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.<\/jats:p>","DOI":"10.1145\/3672557","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T09:20:34Z","timestamp":1718702434000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7001-4123","authenticated-orcid":false,"given":"Andreas","family":"Z\u00fcfle","sequence":"first","affiliation":[{"name":"Emory University, Atlanta, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-0069","authenticated-orcid":false,"given":"Dieter","family":"Pfoser","sequence":"additional","affiliation":[{"name":"Department of Geography and GeoInformation Science, George Mason University, Fairfax, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9275-5336","authenticated-orcid":false,"given":"Carola","family":"Wenk","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tulane University, New Orleans, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5034-6654","authenticated-orcid":false,"given":"Andrew","family":"Crooks","sequence":"additional","affiliation":[{"name":"University at Buffalo, Buffalo, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4307-2381","authenticated-orcid":false,"given":"Hamdi","family":"Kavak","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1145-0608","authenticated-orcid":false,"given":"Taylor","family":"Anderson","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-6698","authenticated-orcid":false,"given":"Joon-Seok","family":"Kim","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3494-0514","authenticated-orcid":false,"given":"Nathan","family":"Holt","sequence":"additional","affiliation":[{"name":"L3Harris Technologies Inc, Melbourne, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5668-4960","authenticated-orcid":false,"given":"Andrew","family":"Diantonio","sequence":"additional","affiliation":[{"name":"L3Harris Technologies Inc, Melbourne, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Amazon AWS. 2023. 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