{"id":"https://openalex.org/W3043790937","doi":"https://doi.org/10.1109/tii.2020.3009280","title":"Variational Graph Neural Networks for Road Traffic Prediction in Intelligent Transportation Systems","display_name":"Variational Graph Neural Networks for Road Traffic Prediction in Intelligent Transportation Systems","publication_year":2020,"publication_date":"2020-07-14","ids":{"openalex":"https://openalex.org/W3043790937","doi":"https://doi.org/10.1109/tii.2020.3009280","mag":"3043790937"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2020.3009280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.3009280","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Transactions on Industrial Informatics","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/A5100403505","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0002-8038-8150"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Zhou","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103161963","display_name":"Qing Yang","orcid":"https://orcid.org/0000-0002-3059-8950"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Yang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034789908","display_name":"Ting Zhong","orcid":"https://orcid.org/0000-0002-8163-3146"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Zhong","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073181706","display_name":"Dajiang Chen","orcid":"https://orcid.org/0000-0003-0745-5836"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dajiang Chen","raw_affiliation_strings":["Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China","Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404886","display_name":"Ning Zhang","orcid":"https://orcid.org/0000-0002-8781-4925"},"institutions":[{"id":"https://openalex.org/I74413500","display_name":"University of Windsor","ror":"https://ror.org/01gw3d370","country_code":"CA","type":"education","lineage":["https://openalex.org/I74413500"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ning Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada","institution_ids":["https://openalex.org/I74413500"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100403505"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":10.1824,"has_fulltext":false,"cited_by_count":163,"citation_normalized_percentile":{"value":0.98895975,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"17","issue":"4","first_page":"2802","last_page":"2812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9897000193595886,"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/computer-science","display_name":"Computer science","score":0.7034358978271484},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5878502130508423},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5612368583679199},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5436784625053406},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5264927744865417},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4851287305355072},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46177589893341064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42910507321357727},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42853254079818726},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4215225279331207},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4211444556713104},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4191584289073944},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3866980969905853},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3781253695487976},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.37229615449905396},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2715945839881897},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22805792093276978},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1491348147392273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7034358978271484},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5878502130508423},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5612368583679199},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5436784625053406},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5264927744865417},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4851287305355072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46177589893341064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42910507321357727},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42853254079818726},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4215225279331207},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4211444556713104},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4191584289073944},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3866980969905853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781253695487976},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37229615449905396},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2715945839881897},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22805792093276978},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1491348147392273},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2020.3009280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.3009280","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1517189076","display_name":null,"funder_award_id":"61502085","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2227486999","display_name":null,"funder_award_id":"61872059","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5453742210","display_name":null,"funder_award_id":"61602097","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":83,"referenced_works":["https://openalex.org/W20141250","https://openalex.org/W63326460","https://openalex.org/W411746464","https://openalex.org/W1522301498","https://openalex.org/W1924770834","https://openalex.org/W1959608418","https://openalex.org/W1994377164","https://openalex.org/W2059671843","https://openalex.org/W2064675550","https://openalex.org/W2079662306","https://openalex.org/W2080731889","https://openalex.org/W2083238230","https://openalex.org/W2117618130","https://openalex.org/W2130942839","https://openalex.org/W2154531209","https://openalex.org/W2165991108","https://openalex.org/W2171234954","https://openalex.org/W2194775991","https://openalex.org/W2519887557","https://openalex.org/W2528639018","https://openalex.org/W2554952599","https://openalex.org/W2587284713","https://openalex.org/W2604436698","https://openalex.org/W2756203131","https://openalex.org/W2789441909","https://openalex.org/W2808535700","https://openalex.org/W2808669017","https://openalex.org/W2889230014","https://openalex.org/W2896797507","https://openalex.org/W2902016061","https://openalex.org/W2903871660","https://openalex.org/W2904449562","https://openalex.org/W2910892140","https://openalex.org/W2919839278","https://openalex.org/W2935726879","https://openalex.org/W2936436865","https://openalex.org/W2945278040","https://openalex.org/W2950099298","https://openalex.org/W2950418200","https://openalex.org/W2950817888","https://openalex.org/W2962790412","https://openalex.org/W2963090522","https://openalex.org/W2963358464","https://openalex.org/W2963755523","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964197766","https://openalex.org/W2965341826","https://openalex.org/W2966009252","https://openalex.org/W2968831808","https://openalex.org/W2969262720","https://openalex.org/W2969280827","https://openalex.org/W2969541706","https://openalex.org/W2970485172","https://openalex.org/W2970912018","https://openalex.org/W2992035660","https://openalex.org/W2999301586","https://openalex.org/W3007912973","https://openalex.org/W3015712039","https://openalex.org/W3103720336","https://openalex.org/W3150807214","https://openalex.org/W4233713109","https://openalex.org/W4322614756","https://openalex.org/W4391602018","https://openalex.org/W6600828923","https://openalex.org/W6602576851","https://openalex.org/W6610566761","https://openalex.org/W6631190155","https://openalex.org/W6640212811","https://openalex.org/W6640963894","https://openalex.org/W6679436768","https://openalex.org/W6726873649","https://openalex.org/W6728547873","https://openalex.org/W6730084236","https://openalex.org/W6733471323","https://openalex.org/W6746015598","https://openalex.org/W6751792830","https://openalex.org/W6752307458","https://openalex.org/W6762634004","https://openalex.org/W6766768147","https://openalex.org/W6767032894","https://openalex.org/W6767341103","https://openalex.org/W6770847983"],"related_works":["https://openalex.org/W4387838477","https://openalex.org/W2067193074","https://openalex.org/W2182785089","https://openalex.org/W4312178642","https://openalex.org/W2375093801","https://openalex.org/W3107426390","https://openalex.org/W1534368937","https://openalex.org/W1925992392","https://openalex.org/W1606995399","https://openalex.org/W4403123063"],"abstract_inverted_index":{"As":[0],"one":[1],"of":[2,7,10,22,59,83,99,102,136],"the":[3,18,64,81,125,137],"most":[4],"important":[5],"applications":[6],"industrial":[8],"Internet":[9],"Things,":[11],"intelligent":[12],"transportation":[13,23],"system":[14],"aims":[15],"to":[16],"improve":[17],"efficiency":[19],"and":[20,56,67,86,96,111],"safety":[21],"networks.":[24],"In":[25],"this":[26],"article,":[27],"we":[28],"propose":[29],"a":[30,75],"novel":[31],"Bayesian":[32],"framework":[33],"entitled":[34],"variational":[35,94],"graph":[36,53],"recurrent":[37],"attention":[38],"neural":[39],"networks":[40],"(VGRAN)":[41],"for":[42],"robust":[43],"traffic":[44,68,88,103,121],"forecasting.":[45],"It":[46],"captures":[47],"time-varying":[48],"road-sensor":[49],"readings":[50],"through":[51],"dynamic":[52],"convolution":[54],"operations":[55],"is":[57,74],"capable":[58],"learning":[60],"latent":[61],"variables":[62],"regarding":[63],"sensor":[65,84],"representation":[66],"sequences.":[69],"The":[70],"proposed":[71,126],"probabilistic":[72],"method":[73],"more":[76],"flexible":[77],"generative":[78],"model":[79,128],"considering":[80],"stochasticity":[82],"attributes":[85],"temporal":[87,113],"correlations.":[89],"Moreover,":[90],"it":[91],"enables":[92],"efficient":[93],"inference":[95],"faithful":[97],"modeling":[98],"implicit":[100],"posteriors":[101],"data,":[104],"which":[105],"are":[106],"usually":[107],"irregular,":[108],"spatial":[109],"correlated,":[110],"multiple":[112],"dependents.":[114],"Extensive":[115],"experiments":[116],"conducted":[117],"on":[118],"two":[119],"real-world":[120],"datasets":[122],"demonstrate":[123],"that":[124],"VGRAN":[127],"outperforms":[129],"state-of-the-art":[130],"approaches":[131],"while":[132],"capturing":[133],"innate":[134],"ambiguity":[135],"predicted":[138],"results.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":45},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}