{"id":"https://openalex.org/W2904449562","doi":"https://doi.org/10.1609/aaai.v33i01.3301890","title":"Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting","display_name":"Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904449562","doi":"https://doi.org/10.1609/aaai.v33i01.3301890","mag":"2904449562"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.3301890","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.3301890","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3877/3755","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3877/3755","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024464918","display_name":"Zulong Diao","orcid":"https://orcid.org/0000-0001-6581-7511"},"institutions":[{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zulong Diao","raw_affiliation_strings":["The State University of New York"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The State University of New York","institution_ids":["https://openalex.org/I1327163397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327844","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0001-8639-3818"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Stony Brook University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook University","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101594820","display_name":"Dafang Zhang","orcid":"https://orcid.org/0000-0003-0765-6857"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dafang Zhang","raw_affiliation_strings":["Hunan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036705554","display_name":"Yingru Liu","orcid":"https://orcid.org/0000-0002-3784-4886"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingru Liu","raw_affiliation_strings":["Stony Brook University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook University","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062119492","display_name":"Kun Xie","orcid":"https://orcid.org/0000-0002-2163-2723"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Xie","raw_affiliation_strings":["Hunan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102074736","display_name":"Shaoyao He","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoyao He","raw_affiliation_strings":["Hunan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":54.5847,"has_fulltext":true,"cited_by_count":321,"citation_normalized_percentile":{"value":0.99849962,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"33","issue":"01","first_page":"890","last_page":"897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9851999878883362,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9398999810218811,"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/laplacian-matrix","display_name":"Laplacian matrix","score":0.7243853807449341},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6839804649353027},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5662837028503418},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4485871493816376},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4354398548603058},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4155470132827759},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3931342661380768},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3783334791660309},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3207022547721863},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17914444208145142}],"concepts":[{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.7243853807449341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6839804649353027},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5662837028503418},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4485871493816376},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4354398548603058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4155470132827759},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3931342661380768},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3783334791660309},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3207022547721863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17914444208145142},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v33i01.3301890","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.3301890","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3877/3755","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/3877","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/3877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.3301890","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.3301890","pdf_url":"https://aaai.org/ojs/index.php/AAAI/article/download/3877/3755","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1531708000","display_name":null,"funder_award_id":"2017JJ1010","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"},{"id":"https://openalex.org/G2144200491","display_name":"NeTS: Small: Fundamental Techniques for Incentive-aware, Efficient, and Reliable Cloudlet Management and Services","funder_award_id":"1526843","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2223155526","display_name":null,"funder_award_id":"CNS-1526843","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G408967044","display_name":"SpecEES:  Collaborative Research:  Spatially Oversampled Dense Multi-Beam Millimeter-Wave Communications for Exponentially Increased Energy-Efficiency","funder_award_id":"1731238","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5398200543","display_name":null,"funder_award_id":"1731238","funder_id":"https://openalex.org/F4320337392","funder_display_name":"Division of Electrical, Communications and Cyber Systems"},{"id":"https://openalex.org/G7973907710","display_name":"\u57fa\u4e8e\u77e9\u9635\u586b\u5145\u7684\u8f6f\u4ef6\u5b9a\u4e49\u7f51\u7edc\u6d41\u91cf\u77e9\u9635\u6d4b\u91cf\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61572184","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8983398099","display_name":"\u57fa\u4e8e\u56fe\u5f62\u5904\u7406\u5355\u5143\u7684\u9ad8\u6027\u80fd\u7f51\u7edc\u5305\u5904\u7406\u6280\u672f\u7814\u7a76","funder_award_id":"61472130","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null},{"id":"https://openalex.org/F4320330206","display_name":"Science and Technology Program of Hunan Province","ror":null},{"id":"https://openalex.org/F4320337392","display_name":"Division of Electrical, Communications and Cyber Systems","ror":"https://ror.org/01krpsy48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W637153065","https://openalex.org/W1662382123","https://openalex.org/W1973943669","https://openalex.org/W2025391890","https://openalex.org/W2101491865","https://openalex.org/W2104849640","https://openalex.org/W2399508263","https://openalex.org/W2406128552","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2528639018","https://openalex.org/W2530386080","https://openalex.org/W2558460151","https://openalex.org/W2560675361","https://openalex.org/W2579495707","https://openalex.org/W2594121863","https://openalex.org/W2611552022","https://openalex.org/W2768008502","https://openalex.org/W2782791108","https://openalex.org/W2790209388","https://openalex.org/W2791999218","https://openalex.org/W2809593532","https://openalex.org/W2963358464","https://openalex.org/W2964015378","https://openalex.org/W2964145825","https://openalex.org/W2964321699","https://openalex.org/W6675594680","https://openalex.org/W6728254797","https://openalex.org/W6780493881"],"related_works":["https://openalex.org/W4287880334","https://openalex.org/W4366700029","https://openalex.org/W3175363083","https://openalex.org/W4293226380","https://openalex.org/W4285230481","https://openalex.org/W4385769873","https://openalex.org/W2015759683","https://openalex.org/W4281634296","https://openalex.org/W4319161863","https://openalex.org/W58770712"],"abstract_inverted_index":{"Graph":[0],"convolutional":[1],"neural":[2],"networks":[3],"(GCNN)":[4],"have":[5],"become":[6],"an":[7],"increasingly":[8],"active":[9],"field":[10],"of":[11,18,45,73,81,84,151],"research.":[12],"It":[13],"models":[14],"the":[15,43,51,55,79,82,107,137,147,152],"spatial":[16,37,56],"dependencies":[17,38,57],"nodes":[19],"in":[20,33],"a":[21,24,63,88,98,118,132,142],"graph":[22,153],"with":[23,87,97,154,173],"pre-defined":[25],"Laplacian":[26,47,85,90,149],"matrix":[27,48,86,91,150],"based":[28,158],"on":[29,126,159],"node":[30],"distances.":[31],"However,":[32],"many":[34],"application":[35],"scenarios,":[36],"change":[39,83],"over":[40],"time,":[41],"and":[42,124,131,163],"use":[44],"fixed":[46],"cannot":[49],"capture":[50],"change.":[52],"To":[53,93],"track":[54],"among":[58],"traffic":[59,69,113,129,138,176],"data,":[60],"we":[61,101],"propose":[62,141],"dynamic":[64,89,148],"spatio-temporal":[65],"GCNN":[66],"for":[67],"accurate":[68],"forecasting.":[70],"The":[71,168],"core":[72],"our":[74,160,165,182],"deep":[75,108],"learning":[76,96,109],"framework":[77],"is":[78,122,171],"finding":[80],"estimator.":[92],"enable":[94],"timely":[95],"low":[99],"complexity,":[100],"creatively":[102],"incorporate":[103],"tensor":[104],"decomposition":[105],"into":[106,117],"framework,":[110],"where":[111],"real-time":[112],"data":[114],"are":[115],"decomposed":[116],"global":[119],"component":[120,134],"that":[121,135,181],"stable":[123],"depends":[125],"long-term":[127],"temporal-spatial":[128],"relationship":[130],"local":[133],"captures":[136],"fluctuations.":[139],"We":[140],"novel":[143],"design":[144,166],"to":[145,187],"estimate":[146],"above":[155],"two":[156,174],"components":[157],"theoretical":[161],"derivation,":[162],"introduce":[164],"basis.":[167],"forecasting":[169],"performance":[170],"evaluated":[172],"realtime":[175],"datasets.":[177],"Experiment":[178],"results":[179],"demonstrate":[180],"network":[183],"can":[184],"achieve":[185],"up":[186],"25%":[188],"accuracy":[189],"improvement.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":39},{"year":2024,"cited_by_count":65},{"year":2023,"cited_by_count":54},{"year":2022,"cited_by_count":65},{"year":2021,"cited_by_count":60},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}