{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T13:16:10Z","timestamp":1778073370173,"version":"3.51.4"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"JST CREST","award":["JPMJCR22M4"],"award-info":[{"award-number":["JPMJCR22M4"]}]},{"name":"JST RISTEX","award":["JPMJRS23K"],"award-info":[{"award-number":["JPMJRS23K"]}]},{"name":"NEDO SIP3","award":["JPJ012495"],"award-info":[{"award-number":["JPJ012495"]}]},{"name":"JSPS KAKENHI","award":["22H03580"],"award-info":[{"award-number":["22H03580"]}]},{"name":"JSPS KAKENHI","award":["22K18422"],"award-info":[{"award-number":["22K18422"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    We propose\n                    <jats:bold>CLIFT<\/jats:bold>\n                    (\n                    <jats:bold>\n                      <jats:underline>C<\/jats:underline>\n                    <\/jats:bold>\n                    ross-City\n                    <jats:bold>\n                      <jats:underline>Lif<\/jats:underline>\n                    <\/jats:bold>\n                    estyle Pattern\n                    <jats:bold>\n                      <jats:underline>T<\/jats:underline>\n                    <\/jats:bold>\n                    ransfer for Human Mobility Prediction), a novel framework that enhances human mobility prediction by integrating general lifestyle patterns shared across cities with city-specific mobility patterns. Accurate human mobility prediction in urban environments is critical for transportation planning, marketing strategies, and disaster response. However, most existing deep learning approaches use only single-city data and exhibit significant performance degradation in small cities with limited training data. These limitations motivate methods that jointly leverage cross-city behavioral patterns and city-specific mobility characteristics. CLIFT addresses this challenge through dual complementary encoders: one captures general lifestyle patterns shared across cities, and the other captures city-specific mobility patterns; their outputs are integrated with a Transformer-based mobility predictor (LP-BERT). This architecture enables the model to jointly capture cross-city transferable behavioral patterns and city-specific mobility characteristics. We evaluated the effectiveness of CLIFT through experiments on the multi-city human mobility dataset LYMob-4Cities, comparing its performance with both single-city and multi-city deep learning-based methods. On average, CLIFT improved GEOBLEU and Top-1 accuracy by 11.1% and 10.6% over the single-city baseline, and by 5.0% and 7.9% over the multi-city baseline, respectively. Furthermore, CLIFT outperformed the top-ranked teams in the international competition,\n                    <jats:italic toggle=\"yes\">Human Mobility Prediction Challenge 2024<\/jats:italic>\n                    , demonstrating superior predictive performance under the same dataset and task setting.\n                  <\/jats:p>","DOI":"10.1145\/3811037","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:55:06Z","timestamp":1776682506000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CLIFT: Cross-City Mobility-Derived Lifestyle Pattern Transfer for Improved Multi-City Next Location Prediction"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0234-5307","authenticated-orcid":false,"given":"Haru","family":"Terashima","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3362-697X","authenticated-orcid":false,"given":"Naoki","family":"Tamura","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5946-2646","authenticated-orcid":false,"given":"Kazuyuki","family":"Shoji","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8985-9043","authenticated-orcid":false,"given":"Tahera","family":"Hossain","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5614-4412","authenticated-orcid":false,"given":"Shin","family":"Katayama","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2906-537X","authenticated-orcid":false,"given":"Kenta","family":"Urano","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9781-0402","authenticated-orcid":false,"given":"Takuro","family":"Yonezawa","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0444-2290","authenticated-orcid":false,"given":"Nobuo","family":"Kawaguchi","sequence":"additional","affiliation":[{"name":"Institutes of Innovation for Future Society, Nagoya University","place":["Nagoya, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"issue":"5","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1068\/b32047","article-title":"Mobile landscapes: Using location data from cell phones for urban analysis","volume":"33","author":"Ratti Carlo","year":"2006","unstructured":"Carlo Ratti, Dennis Frenchman, Riccardo Maria Pulselli, and Sarah Williams. 2006. 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