{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:12:09Z","timestamp":1750306329733,"version":"3.41.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,4,14]],"date-time":"2017-04-14T00:00:00Z","timestamp":1492128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61402360, 61373119, 61332005 and 61402369"],"award-info":[{"award-number":["61402360, 61373119, 61332005 and 61402369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"crossref","award":["2015CB352400"],"award-info":[{"award-number":["2015CB352400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.<\/jats:p>","DOI":"10.1145\/3051128","type":"journal-article","created":{"date-parts":[[2017,4,14]],"date-time":"2017-04-14T12:18:48Z","timestamp":1492172328000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Moving Destination Prediction Using Sparse Dataset"],"prefix":"10.1145","volume":"11","author":[{"given":"Liang","family":"Wang","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an University of Science and Technology, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ku","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Yi","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,4,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.05.070"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-003-0240-0"},{"volume-title":"Proceeding of the 5th Annual ACM\/IEEE International Conference on Mobile Computing and Networking (MobiCom\u201999)","author":"Bhattacharya Amiya","key":"e_1_2_1_3_1"},{"volume-title":"Proceedings of the International Conference on Robotics and Automation (ICRA\u201904)","year":"2004","author":"Allison Bruce","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102363"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2298892"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2010.08.004"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2002.994338"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497415"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2003.1212964"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40846-5_62"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1006\/inco.1996.2612"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2005.1520182"},{"volume-title":"Real Time Destination Prediction Based on Efficient Routes. SAE Technical Paper","author":"Krumm John","key":"e_1_2_1_14_1"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/11853565_15"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2007.141"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/76.510936"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2245914"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2256347"},{"key":"e_1_2_1_20_1","first-page":"2858","article-title":"Jointly dictionary learning for change detection in multispectral imagery","volume":"52","author":"Yuan Y.","year":"2016","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2013.6728224"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s007790200035"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/WAINA.2008.242"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73499-4_50"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2012.6364145"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1529282.1529323"},{"volume-title":"Proceedings of Workshop on Mobile Interaction with the Real World (MIRW\u201907)","year":"2007","author":"Torkkola Kari","key":"e_1_2_1_27_1"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1142008"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2004.1308883"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.06.002"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544830"},{"volume-title":"Solving the data sparsity problem in destination prediction. The VLDB Journal\u2014The International Journal on Very Large Data Bases 24, 2","year":"2013","author":"Xue Andy Yuan","key":"e_1_2_1_32_1"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536274.2536275"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2015.2451515"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2015.2446953"},{"volume-title":"Proceedings of the 10th International Conference on Ubiquitous Computing (Ubicomp\u201908)","author":"Ziebart Brian D.","key":"e_1_2_1_36_1"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3051128","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3051128","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:54:39Z","timestamp":1750222479000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3051128"}},"subtitle":["A Mobility Gradient Descent Approach"],"short-title":[],"issued":{"date-parts":[[2017,4,14]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,8,31]]}},"alternative-id":["10.1145\/3051128"],"URL":"https:\/\/doi.org\/10.1145\/3051128","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2017,4,14]]},"assertion":[{"value":"2016-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-04-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}