{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:01:43Z","timestamp":1778788903260,"version":"3.51.4"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2016,11,2]],"date-time":"2016-11-02T00:00:00Z","timestamp":1478044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Microsoft Research Collaborative Research (CORE) program"},{"name":"JST, Strategic International Collaborative Research Program"},{"name":"Grantin-Aid for Young Scientists of Japan\u2019s Ministry of Education, Culture, Sports, Science, and Technology","award":["26730113"],"award-info":[{"award-number":["26730113"]}]},{"name":"ZENRIN DataCom"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2017,3,31]]},"abstract":"<jats:p>\n            In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users\n            <jats:sup>1<\/jats:sup>\n            over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.\n          <\/jats:p>","DOI":"10.1145\/2970819","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T12:49:04Z","timestamp":1478263744000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Prediction and Simulation of Human Mobility Following Natural Disasters"],"prefix":"10.1145","volume":"8","author":[{"given":"Xuan","family":"Song","sequence":"first","affiliation":[{"name":"The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanshi","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihide","family":"Sekimoto","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryosuke","family":"Shibasaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas Jing","family":"Yuan","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,11,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772698"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0017680"},{"key":"e_1_2_1_3_1","volume-title":"13th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS\u201913)","volume":"33","author":"Bast H."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177697196"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767890"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807197"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"D. Delling J. Dibbelt T. Pajor D. Wagner and R. F. Werneck. 2013. Computing multimodal journeys in practice. In Experimental Algorithms. Springer 260--271.  D. Delling J. Dibbelt T. Pajor D. Wagner and R. F. Werneck. 2013. Computing multimodal journeys in practice. In Experimental Algorithms. Springer 260--271.","DOI":"10.1007\/978-3-642-38527-8_24"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008935410038"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0900282106"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep03997"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2559169"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-011-0244-8"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281230"},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"M. C. Gonzalez C. A. Hidalgo and A.-L. Barabasi. 2008. Understanding individual human mobility patterns. Nature 453 7196 779--782.  M. C. Gonzalez C. A. Hidalgo and A.-L. Barabasi. 2008. Understanding individual human mobility patterns. Nature 453 7196 779--782.","DOI":"10.1038\/nature06958"},{"key":"e_1_2_1_16_1","volume-title":"Cyberpsychology 8 Behavior.","author":"Hahm J."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920934"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835942"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807319"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1203882109"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1756-8765.2009.01028.x"},{"key":"e_1_2_1_22_1","unstructured":"P. M. S. Tan and V. Kumar. 2005. Introduction to Data Mining. Addison Wesley.  P. M. S. Tan and V. Kumar. 2005. Introduction to Data Mining. 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D."},{"key":"e_1_2_1_46_1","volume-title":"Hidden Markov Models for Time Series: An Introduction Using R","author":"Zucchini W."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2970819","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2970819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:40:08Z","timestamp":1750218008000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2970819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,2]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,3,31]]}},"alternative-id":["10.1145\/2970819"],"URL":"https:\/\/doi.org\/10.1145\/2970819","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,2]]},"assertion":[{"value":"2015-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2016-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2016-11-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}