{"id":"https://openalex.org/W2897079037","doi":"https://doi.org/10.1145/3269206.3271708","title":"On Prediction of User Destination by Sub-Trajectory Understanding","display_name":"On Prediction of User Destination by Sub-Trajectory Understanding","publication_year":2018,"publication_date":"2018-10-17","ids":{"openalex":"https://openalex.org/W2897079037","doi":"https://doi.org/10.1145/3269206.3271708","mag":"2897079037"},"language":"en","primary_location":{"id":"doi:10.1145/3269206.3271708","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3271708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103049139","display_name":"Jing Zhao","orcid":"https://orcid.org/0000-0002-6803-5651"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhao","raw_affiliation_strings":["Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086062267","display_name":"Jiajie Xu","orcid":"https://orcid.org/0000-0001-8227-8636"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]},{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajie Xu","raw_affiliation_strings":["Soochow University &amp; Neusoft Corporation, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow University &amp; Neusoft Corporation, Suzhou, China","institution_ids":["https://openalex.org/I3923682","https://openalex.org/I4210134419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032749574","display_name":"Rui Zhou","orcid":"https://orcid.org/0000-0001-6807-4362"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rui Zhou","raw_affiliation_strings":["Swinburne University of Technology, Melbourne , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology, Melbourne , Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016091616","display_name":"Pengpeng Zhao","orcid":"https://orcid.org/0000-0001-6721-6576"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengpeng Zhao","raw_affiliation_strings":["Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028546502","display_name":"Chengfei Liu","orcid":"https://orcid.org/0000-0003-1675-1389"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chengfei Liu","raw_affiliation_strings":["Swinburne University of Technology, Melbourne , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology, Melbourne , Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066727108","display_name":"Feng Zhu","orcid":"https://orcid.org/0000-0003-3542-3989"},"institutions":[{"id":"https://openalex.org/I51629411","display_name":"Siemens (China)","ror":"https://ror.org/00v6g9845","country_code":"CN","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I51629411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhu","raw_affiliation_strings":["Siemens Corporate Technology, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Corporate Technology, Suzhou, China","institution_ids":["https://openalex.org/I51629411"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1413","last_page":"1422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.8258669376373291},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8157318830490112},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7567545175552368},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6576099395751953},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.6384377479553223},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5769782066345215},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5210144519805908},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49190783500671387},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4871230125427246},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44750863313674927},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.434813529253006},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4321698844432831}],"concepts":[{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.8258669376373291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8157318830490112},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7567545175552368},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6576099395751953},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.6384377479553223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5769782066345215},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5210144519805908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49190783500671387},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4871230125427246},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44750863313674927},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.434813529253006},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4321698844432831},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3269206.3271708","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3271708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/120136","is_oa":false,"landing_page_url":"http://hdl.handle.net/2440/120136","pdf_url":null,"source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dx.doi.org/10.1145/3269206.3271708","raw_type":"Conference paper"},{"id":"pmh:oai:researchbank.swinburne.edu.au:3e40dc61-929c-400a-9689-a06771be6554/1","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.3/446658","pdf_url":null,"source":{"id":"https://openalex.org/S4306401157","display_name":"Swinburne Research Bank (Swinburne University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I57093077","host_organization_name":"Swinburne University of Technology","host_organization_lineage":["https://openalex.org/I57093077"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CIKM '18 Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 22-26 October 2018, pp. 1423-1422","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1521536236","https://openalex.org/W1967589533","https://openalex.org/W2021303676","https://openalex.org/W2061491724","https://openalex.org/W2063224314","https://openalex.org/W2080206036","https://openalex.org/W2102113734","https://openalex.org/W2136580807","https://openalex.org/W2147693219","https://openalex.org/W2169481698","https://openalex.org/W2206835661","https://openalex.org/W2250676193","https://openalex.org/W2584293398","https://openalex.org/W2597469141","https://openalex.org/W2601893308","https://openalex.org/W2608536805","https://openalex.org/W2735674392","https://openalex.org/W2740388348","https://openalex.org/W2741460999","https://openalex.org/W2775717462","https://openalex.org/W2788114581","https://openalex.org/W2807894308","https://openalex.org/W2949888546","https://openalex.org/W2963020325"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W4250304930"],"abstract_inverted_index":{"Destination":[0],"prediction":[1,31],"is":[2,103],"known":[3],"as":[4],"an":[5],"important":[6],"problem":[7],"for":[8,54,70],"many":[9],"location":[10],"based":[11],"services":[12],"(LBSs).":[13],"Existing":[14],"solutions":[15],"generally":[16],"apply":[17],"probabilistic":[18],"models":[19,125],"to":[20,37,78,106],"predict":[21],"destinations":[22],"over":[23],"a":[24,45,92],"sub-trajectory,":[25],"but":[26,73],"their":[27],"accuracies":[28],"in":[29],"fine-granularity":[30],"are":[32],"always":[33],"not":[34,58],"satisfactory":[35],"due":[36],"the":[38,63,97,108],"data":[39],"sparsity":[40],"problem.":[41],"This":[42],"paper":[43],"presents":[44],"carefully":[46],"designed":[47],"deep":[48],"learning":[49,101],"model":[50,53,94],"called":[51],"TALL":[52],"destination":[55,86],"prediction.":[56,111],"It":[57],"only":[59],"takes":[60],"advantage":[61],"of":[62,99,110],"bidirectional":[64],"Long":[65],"Short-Term":[66],"Memory":[67],"(LSTM)":[68],"network":[69],"sequence":[71],"modeling,":[72],"also":[74],"gives":[75],"more":[76],"attention":[77,89],"meaningful":[79],"locations":[80],"that":[81,95,122],"have":[82],"strong":[83],"correlations":[84],"w.r.t.":[85],"by":[87],"adopting":[88],"mechanism.":[90],"Furthermore,":[91],"hierarchical":[93],"explores":[96],"fusion":[98],"multi-granularity":[100],"capability":[102],"further":[104],"proposed":[105,124],"improve":[107],"accuracy":[109],"Extensive":[112],"experiments":[113],"on":[114],"Beijing":[115],"and":[116],"Chengdu":[117],"real":[118],"datasets":[119],"finally":[120],"demonstrate":[121],"our":[123],"outperform":[126],"existing":[127],"methods":[128],"without":[129],"considering":[130],"external":[131],"features.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":9}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2018-10-26T00:00:00"}
