{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:46:14Z","timestamp":1771051574376,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"name":"STIC AmSud MOTIF project and EMBRACE Inria associated team"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2021,12,31]]},"abstract":"<jats:p>\n            Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one\u2019s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person\u2019s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how\n            <jats:italic>predictable<\/jats:italic>\n            such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.\n          <\/jats:p>","DOI":"10.1145\/3459625","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T18:05:13Z","timestamp":1624298713000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2804-063X","authenticated-orcid":false,"given":"Douglas Do Couto","family":"Teixeira","sequence":"first","affiliation":[{"name":"Universidade Federal de Minas Gerais, Brazil and Inria, France and Institut Polytechnique de Paris, Palaiseau, France"}]},{"given":"Aline Carneiro","family":"Viana","sequence":"additional","affiliation":[{"name":"Inria, Palaiseau, France"}]},{"given":"Jussara M.","family":"Almeida","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Brazil"}]},{"given":"Mrio S.","family":"Alvim","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10260-005-0121-y"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-018-0510-5"},{"key":"e_1_2_1_3_1","volume-title":"Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5, 1","author":"Beir\u00f3 Mariano G.","year":"2016","unstructured":"Mariano G. Beir\u00f3 , Andr\u00e9 Panisson , Michele Tizzoni , and Ciro Cattuto . 2016. Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5, 1 ( 2016 ). Mariano G. Beir\u00f3, Andr\u00e9 Panisson, Michele Tizzoni, and Ciro Cattuto. 2016. Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5, 1 (2016)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0026857"},{"key":"e_1_2_1_5_1","volume-title":"Marco Fiore, and Carlos Sarraute.","author":"Chen Guangshuo","year":"2019","unstructured":"Guangshuo Chen , Aline Carneiro Viana , Marco Fiore, and Carlos Sarraute. 2019 . Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Sci. (Oct. 2019). Retrieved from https:\/\/hal.inria.fr\/hal-02286080. Guangshuo Chen, Aline Carneiro Viana, Marco Fiore, and Carlos Sarraute. 2019. Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Sci. (Oct. 2019). Retrieved from https:\/\/hal.inria.fr\/hal-02286080."},{"key":"e_1_2_1_6_1","volume-title":"Marco Fiore, and Carlos Sarraute.","author":"Chen Guangshuo","year":"2017","unstructured":"Guangshuo Chen , Sahar Hoteit , Aline Carneiro Viana , Marco Fiore, and Carlos Sarraute. 2017 . Enriching Sparse Mobility Information in Call Detail Records. Technical Report RT-0496. INRIA Saclay-Ile-de-France. Retrieved from https:\/\/hal.inria.fr\/hal-01646608. Guangshuo Chen, Sahar Hoteit, Aline Carneiro Viana, Marco Fiore, and Carlos Sarraute. 2017. Enriching Sparse Mobility Information in Call Detail Records. Technical Report RT-0496. INRIA Saclay-Ile-de-France. Retrieved from https:\/\/hal.inria.fr\/hal-01646608."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-019-0206-8"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/129837"},{"key":"e_1_2_1_9_1","volume-title":"Gonz\u00e1lez","author":"Cuttone Andrea","year":"2018","unstructured":"Andrea Cuttone , Sune Lehmann , and Marta C . Gonz\u00e1lez . 2018 . Understanding predictability and exploration in human mobility. EPJ Data Sci . 7, 1 (2018). Andrea Cuttone, Sune Lehmann, and Marta C. Gonz\u00e1lez. 2018. Understanding predictability and exploration in human mobility. 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Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transport. Res. C: Emerg. Technol. 18, 4 (2010)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2840722"},{"key":"e_1_2_1_20_1","volume-title":"An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6, 1 (19","author":"Ikanovic Edin Lind","year":"2017","unstructured":"Edin Lind Ikanovic and Anders Mollgaard . 2017. An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6, 1 (19 Jun 2017 ), 12. DOI:https:\/\/doi.org\/10.1140\/epjds\/s13688-017-0107-7 Edin Lind Ikanovic and Anders Mollgaard. 2017. An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6, 1 (19 Jun 2017), 12. 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