{"id":"https://openalex.org/W4405933953","doi":"https://doi.org/10.1109/tits.2024.3520511","title":"Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Network","display_name":"Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Network","publication_year":2024,"publication_date":"2024-12-31","ids":{"openalex":"https://openalex.org/W4405933953","doi":"https://doi.org/10.1109/tits.2024.3520511"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3520511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3520511","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","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/A5101641066","display_name":"Yatao Zhang","orcid":"https://orcid.org/0000-0001-5701-2836"},"institutions":[{"id":"https://openalex.org/I4210103465","display_name":"Deutsche Telekom (Singapore)","ror":"https://ror.org/019xzz882","country_code":"SG","type":"company","lineage":["https://openalex.org/I4210093367","https://openalex.org/I4210103465"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Yatao Zhang","raw_affiliation_strings":["Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, CREATE Tower, Singapore","Singapore-ETH Centre, Future Resilient Systems, ETH Zurich, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, CREATE Tower, Singapore","institution_ids":["https://openalex.org/I4210103465"]},{"raw_affiliation_string":"Singapore-ETH Centre, Future Resilient Systems, ETH Zurich, Singapore, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107956871","display_name":"Yi Wang","orcid":"https://orcid.org/0000-0001-7751-0250"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Yi Wang","raw_affiliation_strings":["Civil and Natural Resources Engineering Department, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Civil and Natural Resources Engineering Department, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101938899","display_name":"Song Gao","orcid":"https://orcid.org/0000-0003-4359-6302"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Gao","raw_affiliation_strings":["Department of Geography, Geospatial Data Science Laboratory, University of Wisconsin-Madison, Madison, WI, USA","Department of Geography, Geospatial Data Science Laboratory, University of Wisconsin&#x2013;Madison, Madison, WI, USA"],"affiliations":[{"raw_affiliation_string":"Department of Geography, Geospatial Data Science Laboratory, University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Department of Geography, Geospatial Data Science Laboratory, University of Wisconsin&#x2013;Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004721499","display_name":"Martin Raubal","orcid":"https://orcid.org/0000-0001-5951-6835"},"institutions":[{"id":"https://openalex.org/I4210103465","display_name":"Deutsche Telekom (Singapore)","ror":"https://ror.org/019xzz882","country_code":"SG","type":"company","lineage":["https://openalex.org/I4210093367","https://openalex.org/I4210103465"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Martin Raubal","raw_affiliation_strings":["Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, CREATE Tower, Singapore","Singapore-ETH Centre, Future Resilient Systems, ETH Zurich, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, CREATE Tower, Singapore","institution_ids":["https://openalex.org/I4210103465"]},{"raw_affiliation_string":"Singapore-ETH Centre, Future Resilient Systems, ETH Zurich, Singapore, Singapore","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101641066"],"corresponding_institution_ids":["https://openalex.org/I4210103465"],"apc_list":null,"apc_paid":null,"fwci":3.4257,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.92522498,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"26","issue":"3","first_page":"3885","last_page":"3902"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9898999929428101,"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":0.9898999929428101,"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/T10320","display_name":"Neural Networks and Applications","score":0.9309999942779541,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9122999906539917,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6309201121330261},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.564640998840332},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5430672764778137},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4609139561653137},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.42963269352912903},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.4265187084674835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34752458333969116},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32861024141311646},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1822890043258667},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08133077621459961}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6309201121330261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.564640998840332},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5430672764778137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4609139561653137},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.42963269352912903},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.4265187084674835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34752458333969116},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32861024141311646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1822890043258667},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08133077621459961},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3520511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3520511","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1805473247","https://openalex.org/W2067923159","https://openalex.org/W2073587810","https://openalex.org/W2741267865","https://openalex.org/W2759136286","https://openalex.org/W2901504064","https://openalex.org/W2972535098","https://openalex.org/W3009023036","https://openalex.org/W3021393704","https://openalex.org/W3031047561","https://openalex.org/W3034944009","https://openalex.org/W3109074851","https://openalex.org/W3120491054","https://openalex.org/W3123191313","https://openalex.org/W3133663379","https://openalex.org/W3209643259","https://openalex.org/W3211468413","https://openalex.org/W3213170289","https://openalex.org/W4205459297","https://openalex.org/W4210468880","https://openalex.org/W4252475223","https://openalex.org/W4254555371","https://openalex.org/W4309734258","https://openalex.org/W4310278098","https://openalex.org/W4383371721","https://openalex.org/W4384665479","https://openalex.org/W4386819106","https://openalex.org/W4389478536","https://openalex.org/W4391473819","https://openalex.org/W4392908329","https://openalex.org/W4392939490","https://openalex.org/W6746015598","https://openalex.org/W6781649520","https://openalex.org/W6785773631","https://openalex.org/W6810051882"],"related_works":["https://openalex.org/W2391251536","https://openalex.org/W2961085424","https://openalex.org/W2362198218","https://openalex.org/W2019521278","https://openalex.org/W1984922432","https://openalex.org/W2532801570","https://openalex.org/W4385627933","https://openalex.org/W2480127678","https://openalex.org/W2806270048","https://openalex.org/W4310605282"],"abstract_inverted_index":{"Human":[0],"mobility":[1],"is":[2,113],"intricately":[3],"influenced":[4],"by":[5,66,204,210,219],"urban":[6,49,96],"contexts":[7],"spatially":[8],"and":[9,30,45,70,86,108,136,144,162,181,206,236],"temporally,":[10],"constituting":[11],"essential":[12],"domain":[13],"knowledge":[14,57,260],"in":[15,245],"understanding":[16],"traffic":[17,21,28,63,118,268],"systems.":[18],"While":[19],"existing":[20],"forecasting":[22,65],"models":[23],"primarily":[24],"rely":[25],"on":[26],"raw":[27],"data":[29],"advanced":[31],"deep":[32],"learning":[33],"techniques,":[34],"incorporating":[35],"contextual":[36],"information":[37],"remains":[38],"underexplored":[39],"due":[40],"to":[41,61,91,116,192,240,266],"insufficient":[42],"integration":[43,76],"frameworks":[44],"the":[46,78,84,102,193,198,202,207,226,233,237,248,255],"complexity":[47],"of":[48,89,95,164,229,257],"contexts.":[50,72,97],"This":[51],"study":[52,253],"proposes":[53],"a":[54,74,152],"novel":[55],"context-aware":[56,81,122,259],"graph":[58,109,263],"(CKG)":[59],"framework":[60,79],"enhance":[62],"speed":[64,119],"effectively":[67],"modeling":[68],"spatial":[69,85,143,199],"temporal":[71,87,145,208],"Employing":[73],"relation-dependent":[75,230],"strategy,":[77],"generates":[80],"representations":[82],"from":[83,232,247],"units":[88,215],"CKG":[90],"capture":[92],"spatio-temporal":[93],"dependencies":[94],"A":[98],"CKG-GNN":[99,149],"model,":[100,196],"combining":[101],"CKG,":[103],"dual-view":[104,222],"multi-head":[105],"self-attention":[106],"(MHSA),":[107],"neural":[110,264],"network":[111],"(GNN),":[112],"then":[114],"designed":[115],"predict":[117],"utilizing":[120],"these":[121],"representations.":[123],"Our":[124],"experiments":[125],"demonstrate":[126],"that":[127],"CKG\u2019s":[128],"configuration":[129],"significantly":[130],"influences":[131],"embedding":[132,142],"performance,":[133],"with":[134,262],"ComplEx":[135],"KG2E":[137],"emerging":[138],"as":[139],"optimal":[140],"for":[141,154],"units,":[146],"respectively.":[147,190],"The":[148,221],"model":[150],"establishes":[151],"benchmark":[153],"10-120":[155],"min":[156],"predictions,":[157],"achieving":[158],"average":[159],"MAE,":[160],"MAPE,":[161],"RMSE":[163],"3.46":[165],"<inline-formula":[166,174,183],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[167,175,184],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[168,176,185],"<tex-math":[169,177,186],"notation=\"LaTeX\">$\\pm$</tex-math>":[170,178,187],"</inline-formula>":[171,179,188],"0.01,":[172,189],"14.76":[173],"0.09%,":[180],"5.08":[182],"Compared":[191],"baseline":[194],"DCRNN":[195],"integrating":[197,213],"unit":[200,209],"improves":[201],"MAE":[203],"0.04":[205],"0.13,":[211],"while":[212],"both":[214],"further":[216],"reduces":[217],"it":[218],"0.18.":[220],"MHSA":[223],"analysis":[224],"reveals":[225],"crucial":[227],"role":[228],"features":[231],"context-based":[234],"view":[235],"model\u2019s":[238],"ability":[239],"prioritize":[241],"recent":[242],"time":[243],"slots":[244],"prediction":[246],"sequence-based":[249],"view.":[250],"Overall,":[251],"this":[252],"underscores":[254],"importance":[256],"merging":[258],"graphs":[261],"networks":[265],"improve":[267],"forecasting.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":11}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}