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This article presents a dual-perspective survey of mobility prediction, categorizing models based on their focus on routine, context, or a fusion of both. We review key components of human mobility prediction, explore their applications across multiple domains, survey statistical and machine learning predictors, and empirically evaluate their effectiveness using large-scale mobility data under varying conditions. Based on our empirical results, we offer practical guidelines for selecting mobility prediction techniques suited to different mobility patterns. This survey provides a comprehensive foundation for researchers and practitioners aiming to develop effective human mobility prediction systems across diverse real-world scenarios.<\/jats:p>","DOI":"10.1145\/3770860","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:19:42Z","timestamp":1759835982000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mobile Analytics Techniques: Survey, Evaluation, and Guidelines"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-0211","authenticated-orcid":false,"given":"Kai","family":"Zhao","sequence":"first","affiliation":[{"name":"Walmart AI Lab, Wal-Mart.com USA LLC","place":["Sunnyvale, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6981-8842","authenticated-orcid":false,"given":"Houping","family":"Xiao","sequence":"additional","affiliation":[{"name":"Georgia State University","place":["Atlanta, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3655-7543","authenticated-orcid":false,"given":"Arun","family":"Rai","sequence":"additional","affiliation":[{"name":"Georgia State University","place":["Atlanta, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5864-662X","authenticated-orcid":false,"given":"Jeongmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Georgia State University","place":["Atlanta, United States"]}]}],"member":"320","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"crossref","unstructured":"Abdulrahman Al-Molegi and Antoni Martinez-Balleste. 2022. 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