{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:11:30Z","timestamp":1787065890907,"version":"build-2736575974"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,6,23]],"date-time":"2017-06-23T00:00:00Z","timestamp":1498176000000},"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":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2017,10,31]]},"abstract":"<jats:p>The frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Hence, understanding and predicting human evacuation behavior and mobility will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, existing models are shallow models, and it is difficult to apply them for understanding the \u201cdeep knowledge\u201d of human mobility. Therefore, in this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data), and we build an intelligent system, namely, DeepMob, for understanding and predicting human evacuation behavior and mobility following different types of natural disasters. The key component of DeepMob is based on a deep learning architecture that aims to understand the basic laws that govern human behavior and mobility following natural disasters, from big and heterogeneous data. Furthermore, based on the deep learning model, DeepMob can accurately predict or simulate a person\u2019s future evacuation behaviors or evacuation routes under different disaster conditions. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human mobility following disasters may be predicted and simulated more easily than previously thought.<\/jats:p>","DOI":"10.1145\/3057280","type":"journal-article","created":{"date-parts":[[2017,6,26]],"date-time":"2017-06-26T08:13:22Z","timestamp":1498464802000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["DeepMob"],"prefix":"10.1145","volume":"35","author":[{"given":"Xuan","family":"Song","sequence":"first","affiliation":[{"name":"The University of Tokyo, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryosuke","family":"Shibasaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholos Jing","family":"Yuan","sequence":"additional","affiliation":[{"name":"Microsoft Research, Haidian District Beijing, P.R. 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