{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T18:23:30Z","timestamp":1758824610492,"version":"3.41.0"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T00:00:00Z","timestamp":1545868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100013102","name":"Australian Research Council","doi-asserted-by":"publisher","award":["LP150100246"],"award-info":[{"award-number":["LP150100246"]}],"id":[{"id":"10.13039\/100013102","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,12,27]]},"abstract":"<jats:p>Understanding and predicting human mobility is vital to a large number of applications, ranging from recommendations to safety and urban service planning. In some travel applications, the ability to accurately predict the user's future trajectory is vital for delivering high quality of service. The accurate prediction of detailed trajectories would empower location-based service providers with the ability to deliver more precise recommendations to users. Existing work on human mobility prediction has mainly focused on the prediction of the next location (or the set of locations) visited by the user, rather than on the prediction of the continuous trajectory (sequences of further locations and the corresponding arrival and departure times). Furthermore, existing approaches often return predicted locations as regions with coarse granularity rather than geographical coordinates, which limits the practicality of the prediction.<\/jats:p>\n          <jats:p>In this paper, we introduce a novel trajectory prediction problem: given historical data and a user's initial trajectory in the morning, can we predict the user's full trajectory later in the day (e.g. the afternoon trajectory)? The predicted continuous trajectory includes the sequence of future locations, the stay times, and the departure times. We first conduct a comprehensive analysis about the relationship between morning trajectories and the corresponding afternoon trajectories, and found there is a positive correlation between them. Our proposed method combines similarity metrics over the extracted temporal sequences of locations to estimate similar informative segments across user trajectories.<\/jats:p>\n          <jats:p>Our evaluation shows results on both labeled and geographical trajectories with a prediction error reduced by 10-35% in comparison to the baselines. This improvement has the potential to enable precise location services, raising usefulness to users to unprecedented levels. We also present empirical evaluations with Markov model and Long Short Term Memory (LSTM), a state-of-the-art Recurrent Neural Network model. Our proposed method is shown to be more effective when smaller number of samples are used and is exponentially more efficient than LSTM.<\/jats:p>","DOI":"10.1145\/3287064","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T19:28:03Z","timestamp":1545938883000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["What Will You Do for the Rest of the Day?"],"prefix":"10.1145","volume":"2","author":[{"given":"Amin","family":"Sadri","sequence":"first","affiliation":[{"name":"RMIT University, Computer Science and IT, School of Science, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[{"name":"RMIT University, Computer Science and IT, School of Science, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongli","family":"Ren","sequence":"additional","affiliation":[{"name":"RMIT University, Computer Science and IT, School of Science, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"RMIT University, Computer Science and IT, School of Science, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John C.","family":"Krumm","sequence":"additional","affiliation":[{"name":"Microsoft Research, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"University of Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2011.60"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2093973.2093979"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-003-0240-0"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858557"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2093973.2094034"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013759724438"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2010.5625119"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2010.08.004"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066213"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06605-9_16"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2012.6195673"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/989684.989696"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2002.1160085"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/42.6.473"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1978942.1979119"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2013.07.008"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2014.09.001"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2013.03.006"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00265-009-0739-0"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_13"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2181196.2181199"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172122"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2442810.2442821"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature06958"},{"key":"e_1_2_2_26_1","first-page":"215","volume-title":"proceedings of the 17th international conference on data engineering","author":"Han J.","year":"2001"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335372"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2756450"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497415"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-010-0181-y"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347153"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1771110.1771163"},{"volume-title":"Proc. ICPS","year":"2010","author":"Kiukkonen N.","key":"e_1_2_2_34_1"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304889.3304985"},{"volume-title":"A markov model for driver turn prediction","year":"2016","author":"Krumm J.","key":"e_1_2_2_36_1"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.5555\/2021975.2021984"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/11853565_15"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1132905.1132915"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2011.61"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/2125163.2125192"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01193332"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015841"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2013.6567123"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370421"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2016.03.053"},{"key":"e_1_2_2_47_1","doi-asserted-by":"crossref","unstructured":"J. Meng Y. Hu G. Shou Z. Guo and J. Huang. Research on wifi user dwell time distribution. In Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) 2015 IEEE 12th Intl Conf on pages 1120--1126. IEEE 2015.  J. Meng Y. Hu G. Shou Z. Guo and J. Huang. Research on wifi user dwell time distribution. In Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) 2015 IEEE 12th Intl Conf on pages 1120--1126. IEEE 2015.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.205"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557091"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73499-4_50"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409944.1409952"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/11823285_96"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.12.041"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2653481.2653491"},{"volume-title":"AAAI","year":"2012","author":"Sadilek A.","key":"e_1_2_2_54_1"},{"key":"e_1_2_2_55_1","unstructured":"A. Sadri Y. Ren and F. D. Salim. Information gain-based metric for recognizing transitions in human activities. Pervasive and Mobile Computing.  A. Sadri Y. Ren and F. D. Salim. Information gain-based metric for recognizing transitions in human activities. Pervasive and Mobile Computing."},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123024.3123140"},{"key":"e_1_2_2_57_1","first-page":"152","volume-title":"NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems","author":"Scellato S.","year":"2011"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2030112.2030151"},{"volume-title":"BLEDoorGuard: A device-free person identification framework using bluetooth signals for door access","year":"2018","author":"Shao W.","key":"e_1_2_2_59_1"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2017.16"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1177170"},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2006.185"},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2006.13"},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2782759.2782767"},{"volume-title":"Proceedings of the Mobile Data Challenge Workshop (MDC 2012","year":"1821","author":"Tran L. H.","key":"e_1_2_2_65_1"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.11.002"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2627534.2627553"},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020581"},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-1286(01)00269-9"},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.5555\/1996794.1996807"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3287064","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3287064","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:08Z","timestamp":1750208528000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3287064"}},"subtitle":["An Approach to Continuous Trajectory Prediction"],"short-title":[],"issued":{"date-parts":[[2018,12,27]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,12,27]]}},"alternative-id":["10.1145\/3287064"],"URL":"https:\/\/doi.org\/10.1145\/3287064","relation":{},"ISSN":["2474-9567"],"issn-type":[{"type":"electronic","value":"2474-9567"}],"subject":[],"published":{"date-parts":[[2018,12,27]]},"assertion":[{"value":"2018-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-12-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}