{"id":"https://openalex.org/W4385938094","doi":"https://doi.org/10.1109/jiot.2023.3305006","title":"Vehicular Crowdsensing Inference and Prediction With Multi Training Graph Transformer Networks","display_name":"Vehicular Crowdsensing Inference and Prediction With Multi Training Graph Transformer Networks","publication_year":2023,"publication_date":"2023-08-17","ids":{"openalex":"https://openalex.org/W4385938094","doi":"https://doi.org/10.1109/jiot.2023.3305006"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2023.3305006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3305006","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","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/A5079280268","display_name":"Jie Huo","orcid":"https://orcid.org/0000-0001-6171-4654"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Huo","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6171-4654","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057155774","display_name":"Luhan Wang","orcid":"https://orcid.org/0000-0002-7056-5416"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luhan Wang","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7056-5416","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019111566","display_name":"Zhaoming Lu","orcid":"https://orcid.org/0000-0002-0182-1770"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoming Lu","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0182-1770","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000851572","display_name":"Xiangming Wen","orcid":"https://orcid.org/0000-0003-2793-6696"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangming Wen","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2793-6696","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.7688,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.64379289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":"1","first_page":"217","last_page":"227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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":0.8441832661628723},{"id":"https://openalex.org/keywords/crowdsensing","display_name":"Crowdsensing","score":0.8248520493507385},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7985084056854248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5291205644607544},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4993894100189209},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4838041663169861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4674105644226074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4632125794887543},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10093703866004944},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.0851275622844696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8441832661628723},{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.8248520493507385},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7985084056854248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5291205644607544},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4993894100189209},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4838041663169861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4674105644226074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4632125794887543},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10093703866004944},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.0851275622844696},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2023.3305006","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3305006","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G875366402","display_name":null,"funder_award_id":"2022YFB25-03202","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1971402834","https://openalex.org/W1978695314","https://openalex.org/W2070807660","https://openalex.org/W2079204536","https://openalex.org/W2153458569","https://openalex.org/W2584274610","https://openalex.org/W2766774498","https://openalex.org/W2788134583","https://openalex.org/W2789572951","https://openalex.org/W2893733132","https://openalex.org/W2895387432","https://openalex.org/W2897520559","https://openalex.org/W2943584607","https://openalex.org/W2949041559","https://openalex.org/W2953002421","https://openalex.org/W2964015378","https://openalex.org/W2972004543","https://openalex.org/W2998028256","https://openalex.org/W3002405259","https://openalex.org/W3042557051","https://openalex.org/W3087394118","https://openalex.org/W3087906567","https://openalex.org/W3089196758","https://openalex.org/W3092147969","https://openalex.org/W3109901740","https://openalex.org/W3110669662","https://openalex.org/W3120286259","https://openalex.org/W3130452720","https://openalex.org/W3156080831","https://openalex.org/W3175739563","https://openalex.org/W3178771377","https://openalex.org/W3182874219","https://openalex.org/W3200036893","https://openalex.org/W3213533873","https://openalex.org/W4205409847","https://openalex.org/W4211202585","https://openalex.org/W4225265332","https://openalex.org/W4283210806","https://openalex.org/W4295665208","https://openalex.org/W4297733535","https://openalex.org/W4300006734","https://openalex.org/W6726873649","https://openalex.org/W6739901393","https://openalex.org/W6783310704"],"related_works":["https://openalex.org/W2563347706","https://openalex.org/W2608033733","https://openalex.org/W4287604253","https://openalex.org/W1894261276","https://openalex.org/W2168877220","https://openalex.org/W2295662171","https://openalex.org/W4206178588","https://openalex.org/W3094491777","https://openalex.org/W3214715529","https://openalex.org/W4287635093"],"abstract_inverted_index":{"Vehicular":[0],"crowdsensing":[1,40,66],"has":[2,41],"emerged":[3],"as":[4],"a":[5,197],"prominent":[6],"sensing":[7,18,36,50,73,88,132,171,178],"paradigm":[8],"in":[9,223],"the":[10,31,35,70,82,85,125,131,158,166,170,175,183,187,191,205,215,219],"Internet":[11],"of":[12,84,91,169,207,221],"Things":[13],"(IoT),":[14],"and":[15,19,52,111,118,127,140,173,226],"its":[16],"powerful":[17],"computing":[20],"capabilities":[21],"can":[22],"provide":[23],"sufficient":[24],"data":[25,47,58,74,116,172,224],"for":[26,59,120],"various":[27],"applications.":[28],"To":[29],"reduce":[30],"cost":[32],"while":[33],"ensuring":[34],"quality,":[37],"sparse":[38],"mobile":[39],"been":[42],"proposed,":[43],"which":[44,149],"only":[45,69],"requires":[46],"from":[48,186],"some":[49],"areas":[51],"utilizes":[53,135],"spatiotemporal":[54],"correlation":[55,168],"to":[56,77,113,144,151,164,181,190],"infer":[57],"other":[60],"unsensed":[61],"areas.":[62],"In":[63,94],"real":[64],"vehicular":[65,121],"scenarios,":[67],"not":[68],"current":[71],"period":[72],"is":[75,90,162],"required":[76],"be":[78],"inferred":[79],"but":[80],"also":[81],"prediction":[83,119,193],"future":[86,176],"whole":[87],"map":[89],"great":[92],"significance.":[93],"this":[95],"article,":[96],"we":[97,195],"propose":[98,196],"multitask":[99],"pretraining":[100],"graph":[101,107,141],"transformer":[102,112,156],"networks":[103,109,143],"(MT-PTGTN)":[104],"that":[105,202],"incorporate":[106],"neural":[108],"(GNNs)":[110],"support":[114],"both":[115],"inference":[117,153,188,225],"crowdsensing.":[122],"Comprehensively":[123],"considering":[124],"surface":[126],"underlying":[128],"patterns":[129],"among":[130],"grids,":[133],"MT-PTGTN":[134,222],"pre-training":[136],"topological":[137],"mining":[138],"GNNs":[139],"attention":[142,160],"model":[145],"two":[146],"patterns,":[147],"respectively,":[148],"contributes":[150],"improving":[152],"accuracy.":[154],"The":[155,211],"with":[157],"multilayer":[159],"mechanism":[161],"incorporated":[163],"capture":[165],"temporal":[167],"predict":[174],"complete":[177],"map.":[179],"Furthermore,":[180],"prevent":[182],"error":[184],"propagation":[185],"task":[189],"subsequent":[192],"task,":[194],"dynamic":[198],"multi-task":[199],"learning":[200],"framework":[201],"dynamically":[203],"adjusts":[204],"weights":[206],"tasks":[208],"during":[209],"training.":[210],"experimental":[212],"evaluation":[213],"on":[214],"real-world":[216],"dataset":[217],"demonstrates":[218],"superiority":[220],"prediction.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-23T07:36:19.812096","created_date":"2025-10-10T00:00:00"}