{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:59:13Z","timestamp":1775325553712,"version":"3.50.1"},"reference-count":26,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"5968","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Science"],"published-print":{"date-parts":[[2010,2,19]]},"abstract":"<jats:title>Predictable Travel Routines<\/jats:title><jats:p>While people rarely perceive their actions to be random, current models of human activity are fundamentally stochastic. Processes that rely on human mobility patterns, like the prediction of new epidemics, traffic engineering, or city planning, could benefit from highly accurate predictive models. To investigate the predictability of human dynamics,<jats:bold>Song<jats:italic>et al.<\/jats:italic><\/jats:bold>(p.<jats:related-article xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"doi\" page=\"1018\" related-article-type=\"in-this-issue\" vol=\"327\" xlink:href=\"10.1126\/science.1177170\">1018<\/jats:related-article>) used the recorded trajectories of millions of mobile phone users, collected by mobile phone companies and anonymized for research purposes. They hypothesized that given the wide range of travel patterns that different users follow, there would be significant differences between their predictability as well: Users who travel less should be easier to predict than those who are constantly on the road. Surprisingly, there was 93% predictability across the whole user base, and individuals' predictability did not in general fall significantly below 80%.<\/jats:p>","DOI":"10.1126\/science.1177170","type":"journal-article","created":{"date-parts":[[2010,2,18]],"date-time":"2010-02-18T21:05:11Z","timestamp":1266527111000},"page":"1018-1021","source":"Crossref","is-referenced-by-count":2495,"title":["Limits of Predictability in Human Mobility"],"prefix":"10.1126","volume":"327","author":[{"given":"Chaoming","family":"Song","sequence":"first","affiliation":[{"name":"Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA."},{"name":"Department of Medicine, Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA."}]},{"given":"Zehui","family":"Qu","sequence":"additional","affiliation":[{"name":"Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA."},{"name":"Department of Medicine, Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA."},{"name":"School of Computer Science and Engineering, University of Electric Science and Technology of China, Chengdu 610054, China."}]},{"given":"Nicholas","family":"Blumm","sequence":"additional","affiliation":[{"name":"Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA."},{"name":"Department of Medicine, Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA."}]},{"given":"Albert-L\u00e1szl\u00f3","family":"Barab\u00e1si","sequence":"additional","affiliation":[{"name":"Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA."},{"name":"Department of Medicine, Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.81.591"},{"key":"e_1_3_2_3_2","first-page":"33","article-title":"The theory of probabilities and telephone conversations export","volume":"20","author":"Erlang A. 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