{"id":"https://openalex.org/W2997425230","doi":"https://doi.org/10.1109/icct46805.2019.8947062","title":"Statistics-Enhanced Destination Prediction Model for Multi-Users Based on Deep Learning","display_name":"Statistics-Enhanced Destination Prediction Model for Multi-Users Based on Deep Learning","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2997425230","doi":"https://doi.org/10.1109/icct46805.2019.8947062","mag":"2997425230"},"language":"en","primary_location":{"id":"doi:10.1109/icct46805.2019.8947062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct46805.2019.8947062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","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/A5103130384","display_name":"Chujie Wang","orcid":"https://orcid.org/0000-0003-1836-7962"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chujie Wang","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014842979","display_name":"Rongpeng Li","orcid":"https://orcid.org/0000-0003-4297-5060"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongpeng Li","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036831868","display_name":"Zhifeng Zhao","orcid":"https://orcid.org/0000-0002-5479-7890"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhifeng Zhao","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100626780","display_name":"Honggang Zhang","orcid":"https://orcid.org/0000-0003-1492-1364"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honggang Zhang","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.24971009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"1385","last_page":"1390"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9848999977111816,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9765999913215637,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.818771243095398},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.7246337532997131},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5436382293701172},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5283041000366211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5258983969688416},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4963393807411194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4818881154060364},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4529886543750763},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.45167863368988037},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4212125837802887},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4206883907318115},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.41230762004852295},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17911383509635925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08237040042877197}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.818771243095398},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.7246337532997131},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5436382293701172},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5283041000366211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5258983969688416},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4963393807411194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4818881154060364},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4529886543750763},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.45167863368988037},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4212125837802887},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4206883907318115},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41230762004852295},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17911383509635925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08237040042877197},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icct46805.2019.8947062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct46805.2019.8947062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1626398438","https://openalex.org/W1673310716","https://openalex.org/W1689711448","https://openalex.org/W2102113734","https://openalex.org/W2115450697","https://openalex.org/W2125283600","https://openalex.org/W2130942839","https://openalex.org/W2144758644","https://openalex.org/W2155856926","https://openalex.org/W2212703438","https://openalex.org/W2250676193","https://openalex.org/W2293182310","https://openalex.org/W2539781657","https://openalex.org/W2583189593","https://openalex.org/W2788114581","https://openalex.org/W2962953297","https://openalex.org/W2963266340","https://openalex.org/W6636500457","https://openalex.org/W6637131181","https://openalex.org/W6675365184","https://openalex.org/W6679436768","https://openalex.org/W6688167117"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2586280620","https://openalex.org/W2805505483","https://openalex.org/W2334071950","https://openalex.org/W2384744344","https://openalex.org/W4233932308","https://openalex.org/W1799694159","https://openalex.org/W2393169196","https://openalex.org/W2366610330","https://openalex.org/W4242143973"],"abstract_inverted_index":{"Destination":[0],"prediction":[1,40],"belongs":[2],"to":[3,42,78,105],"one":[4],"of":[5,46,127],"the":[6,19,44,66,68,94,101,110,123,128],"fundamental":[7],"tasks":[8],"for":[9,48,83],"many":[10,14],"location-based":[11],"services.":[12],"However,":[13],"existing":[15],"methods":[16],"suffer":[17],"from":[18,58],"data":[20],"sparsity":[21],"problem":[22],"or":[23],"poor":[24],"generalization":[25],"problem.":[26],"To":[27],"avoid":[28],"these":[29],"problems,":[30],"this":[31],"paper":[32],"proposes":[33],"a":[34,59,90,119],"deep":[35],"learning-based":[36],"and":[37,72,113],"statistics-enhanced":[38,91],"destination":[39,85],"framework":[41],"predict":[43],"destinations":[45],"trajectories":[47],"different":[49],"users":[50],"simultaneously.":[51],"We":[52,87],"first":[53],"analyze":[54],"human":[55],"mobility":[56,74],"characteristics":[57],"real":[60],"world":[61],"GPS":[62],"dataset.":[63],"Based":[64],"on":[65,118],"analysis,":[67],"shared":[69],"spatio-temporal":[70],"regularities":[71],"user":[73],"preferences":[75],"are":[76],"combined":[77],"provide":[79],"effective":[80],"statistic":[81,114],"information":[82],"multi-user":[84],"prediction.":[86],"then":[88],"propose":[89],"model":[92],"where":[93],"Long":[95],"Short-Term":[96],"Memory":[97],"(LSTM)":[98],"network":[99],"is":[100],"most":[102],"critical":[103],"component":[104],"establish":[106],"complex":[107],"dependencies":[108],"between":[109],"trajectory":[111],"sequence":[112],"features.":[115],"Experimental":[116],"results":[117],"realistic":[120],"dataset":[121],"demonstrate":[122],"significant":[124],"performance":[125],"improvement":[126],"proposed":[129],"framework.":[130]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
