{"id":"https://openalex.org/W4407574868","doi":"https://doi.org/10.1109/tits.2025.3537637","title":"A Task-Oriented Spatial Graph Structure Learning Method for Traffic Forecasting","display_name":"A Task-Oriented Spatial Graph Structure Learning Method for Traffic Forecasting","publication_year":2025,"publication_date":"2025-02-14","ids":{"openalex":"https://openalex.org/W4407574868","doi":"https://doi.org/10.1109/tits.2025.3537637"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3537637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3537637","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/A5109301657","display_name":"Ting Wang","orcid":"https://orcid.org/0009-0007-2559-0843"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Wang","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0007-2559-0843","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035948567","display_name":"Shengjie Zhao","orcid":"https://orcid.org/0000-0002-6109-2522"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengjie Zhao","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6109-2522","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078255068","display_name":"Wenzhen Jia","orcid":"https://orcid.org/0000-0002-3730-2622"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhen Jia","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3730-2622","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071224169","display_name":"Daqian Shi","orcid":"https://orcid.org/0000-0003-2183-1957"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daqian Shi","raw_affiliation_strings":["Institute of Health Informatics, University College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0003-2183-1957","affiliations":[{"raw_affiliation_string":"Institute of Health Informatics, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.9576,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.98035721,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"26","issue":"4","first_page":"4770","last_page":"4779"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9451000094413757,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9451000094413757,"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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9442999958992004,"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/T11106","display_name":"Data Management and Algorithms","score":0.9351000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6615926027297974},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4514648914337158},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44851309061050415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44661515951156616},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.4360373020172119},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37339574098587036},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27366024255752563},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2075842320919037},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13275861740112305},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.08033007383346558}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615926027297974},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4514648914337158},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44851309061050415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44661515951156616},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.4360373020172119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37339574098587036},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27366024255752563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2075842320919037},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13275861740112305},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.08033007383346558},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tits.2025.3537637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3537637","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"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10205456","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10205456/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems pp. 1-10. (2025)","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2976102682","display_name":null,"funder_award_id":"2023YFC3806002","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5031841560","display_name":null,"funder_award_id":"2023YFC3806000","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7761990065","display_name":null,"funder_award_id":"61936014","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2025391890","https://openalex.org/W2036785686","https://openalex.org/W2064675550","https://openalex.org/W2165991108","https://openalex.org/W2528639018","https://openalex.org/W2530386080","https://openalex.org/W2531473348","https://openalex.org/W2563118655","https://openalex.org/W2579495707","https://openalex.org/W2604862142","https://openalex.org/W2734601503","https://openalex.org/W2754252319","https://openalex.org/W2756203131","https://openalex.org/W2808862972","https://openalex.org/W2903871660","https://openalex.org/W2904449562","https://openalex.org/W2953583753","https://openalex.org/W2965341826","https://openalex.org/W2965411974","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W2998559444","https://openalex.org/W3024560045","https://openalex.org/W3035805403","https://openalex.org/W3080253043","https://openalex.org/W3081203761","https://openalex.org/W3096005429","https://openalex.org/W3123191313","https://openalex.org/W3153206160","https://openalex.org/W3169450514","https://openalex.org/W4220977700","https://openalex.org/W4221166060","https://openalex.org/W4233713109","https://openalex.org/W4235019172","https://openalex.org/W4306317966","https://openalex.org/W4312619307","https://openalex.org/W4312758597","https://openalex.org/W4320481403","https://openalex.org/W4382239616","https://openalex.org/W4382449675","https://openalex.org/W4385652073","https://openalex.org/W6679436768","https://openalex.org/W6746015598","https://openalex.org/W6760886919"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Traffic":[0],"forecasting":[1,213],"is":[2,168,208,222],"the":[3,30,34,53,136,142,147,151,192,205,211,218,233,246,249],"foundation":[4],"of":[5,55,138,154,195,230,248],"intelligent":[6],"transportation":[7],"systems":[8],"(ITS).":[9],"In":[10,70,189],"recent,":[11],"graph":[12,31,42,49,93,96,104,111,124,143,175,184,221],"neural":[13],"networks":[14],"(GNNs)":[15],"have":[16],"successfully":[17],"captured":[18],"spatial-temporal":[19,57],"dependencies":[20,58,89,116],"to":[21,46,86,99,112,129,170,199,215,224],"forecast":[22],"traffic":[23,27,118,212,239],"conditions":[24],"by":[25,60,90],"transforming":[26],"data":[28],"in":[29,117],"domain.":[32],"Nevertheless,":[33],"existing":[35],"methods":[36,100],"focus":[37],"only":[38],"on":[39,126,237],"learning":[40,92,185],"informative":[41,48],"representations":[43],"and":[44,67,95,150],"fail":[45],"model":[47,114,186],"structures,":[50],"which":[51,84,244],"hinders":[52],"capture":[54,87,200],"dynamic":[56,61,88,115,162],"caused":[59],"factors":[62],"such":[63],"as":[64],"weather,":[65],"accidents,":[66],"special":[68],"events.":[69],"this":[71,190,225],"paper,":[72],"we":[73,106,121],"propose":[74],"a":[75,108,173,180],"novel":[76],"task-oriented":[77],"Spatial":[78],"Graph":[79],"Structure":[80],"Learning":[81],"(SGSL)":[82],"method,":[83],"aims":[85],"jointly":[91],"structures":[94],"representations.":[97],"Compared":[98,227],"that":[101,217],"use":[102],"spectral":[103],"representations,":[105],"exploit":[107],"learnable":[109],"spatial":[110,127,139,174,220],"effectively":[113,160],"data.":[119],"Moreover,":[120],"directly":[122],"define":[123],"convolutions":[125],"relations":[128,155],"specify":[130],"different":[131],"edge":[132],"weights":[133,153],"when":[134],"aggregating":[135],"information":[137],"neighbours.":[140],"Thus,":[141],"structure":[144],"alterations,":[145],"i.e.,":[146],"relation":[148],"changes,":[149],"time-varying":[152],"can":[156],"be":[157],"encapsulated,":[158],"thereby":[159],"representing":[161],"dependencies.":[163],"The":[164],"gradient":[165],"descent":[166],"strategy":[167],"introduced":[169],"periodically":[171],"learn":[172],"through":[176],"joint":[177],"optimization":[178,206],"with":[179,228],"newly":[181],"designed":[182],"deep":[183],"named":[187],"GAT-nLSTM.":[188],"manner,":[191],"intrinsic":[193],"behaviours":[194],"nodes":[196],"are":[197],"learned":[198,219],"correlations":[201],"across":[202],"periods.":[203],"Notably,":[204],"process":[207],"performed":[209],"under":[210],"constraint":[214],"ensure":[216],"specific":[223],"task.":[226],"those":[229],"state-of-the-art":[231],"baselines,":[232],"experimental":[234],"results":[235],"obtained":[236],"real-world":[238],"datasets":[240],"show":[241],"significant":[242],"improvement,":[243],"verifies":[245],"superiority":[247],"proposed":[250],"SGSL.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
