{"id":"https://openalex.org/W2756203131","doi":"https://doi.org/10.24963/ijcai.2018/505","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","display_name":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2756203131","doi":"https://doi.org/10.24963/ijcai.2018/505","mag":"2756203131"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/505","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/505","pdf_url":"https://www.ijcai.org/proceedings/2018/0505.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0505.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bing Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Yu","raw_affiliation_strings":["School of Mathematical Sciences, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haoteng Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoteng Yin","raw_affiliation_strings":["Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China","Center for Data Science, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Center for Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhanxing Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanxing Zhu","raw_affiliation_strings":["Beijing Institute of Big Data Research (BIBDR), Beijing, China","Center for Data Science, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Big Data Research (BIBDR), Beijing, China","institution_ids":["https://openalex.org/I4210096250"]},{"raw_affiliation_string":"Center for Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":92.0582,"has_fulltext":true,"cited_by_count":3253,"citation_normalized_percentile":{"value":0.99994778,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3634","last_page":"3640"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9937999844551086,"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":0.9914000034332275,"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/deep-learning","display_name":"Deep learning","score":0.626800000667572},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.590399980545044},{"id":"https://openalex.org/keywords/traffic-speed","display_name":"Traffic speed","score":0.3849000036716461},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3792000114917755},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.352400004863739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7530999779701233},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.626800000667572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6227999925613403},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.590399980545044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48429998755455017},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3662000000476837},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C2988166257","wikidata":"https://www.wikidata.org/wiki/Q924286","display_name":"Traffic network","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2924000024795532}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.24963/ijcai.2018/505","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/505","pdf_url":"https://www.ijcai.org/proceedings/2018/0505.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1709.04875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.04875","pdf_url":"https://arxiv.org/pdf/1709.04875","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:eprints.soton.ac.uk:486045","is_oa":false,"landing_page_url":"https://eprints.soton.ac.uk/486045/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401019","display_name":"ePrints Soton (University of Southampton)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I43439940","host_organization_name":"University of Southampton","host_organization_lineage":["https://openalex.org/I43439940"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Book Section"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/505","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/505","pdf_url":"https://www.ijcai.org/proceedings/2018/0505.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2756203131.pdf","grobid_xml":"https://content.openalex.org/works/W2756203131.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W1485009520","https://openalex.org/W1973943669","https://openalex.org/W1983883318","https://openalex.org/W2036785686","https://openalex.org/W2101491865","https://openalex.org/W2130942839","https://openalex.org/W2158787690","https://openalex.org/W2165991108","https://openalex.org/W2297059404","https://openalex.org/W2406128552","https://openalex.org/W2468907370","https://openalex.org/W2531473348","https://openalex.org/W2563118655","https://openalex.org/W2768008502","https://openalex.org/W6622866007","https://openalex.org/W6791748544","https://openalex.org/W6803771590","https://openalex.org/W6863994431"],"related_works":[],"abstract_inverted_index":{"Timely":[0],"accurate":[1],"traffic":[2,8,20,63,110,120],"forecast":[3],"is":[4],"crucial":[5],"for":[6],"urban":[7],"control":[9],"and":[10,17,33,37,70,79,112],"guidance.":[11],"Due":[12],"to":[13,55],"the":[14,26,57,75,81],"high":[15],"nonlinearity":[16],"complexity":[18],"of":[19,28,66],"flow,":[21],"traditional":[22],"methods":[23],"cannot":[24],"satisfy":[25],"requirements":[27],"mid-and-long":[29],"term":[30],"prediction":[31,60],"tasks":[32],"often":[34],"neglect":[35],"spatial":[36],"temporal":[38],"dependencies.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43,73],"propose":[44],"a":[45],"novel":[46],"deep":[47],"learning":[48],"framework,":[49],"Spatio-Temporal":[50],"Graph":[51],"Convolutional":[52],"Networks":[53],"(STGCN),":[54],"tackle":[56],"time":[58],"series":[59],"problem":[61,76],"in":[62],"domain.":[64],"Instead":[65],"applying":[67],"regular":[68],"convolutional":[69,85],"recurrent":[71],"units,":[72],"formulate":[74],"on":[77,117],"graphs":[78],"build":[80],"model":[82,100],"with":[83,93],"complete":[84],"structures,":[86],"which":[87],"enable":[88],"much":[89],"faster":[90],"training":[91],"speed":[92],"fewer":[94],"parameters.":[95],"Experiments":[96],"show":[97],"that":[98],"our":[99],"STGCN":[101],"effectively":[102],"captures":[103],"comprehensive":[104],"spatio-temporal":[105],"correlations":[106],"through":[107],"modeling":[108],"multi-scale":[109],"networks":[111],"consistently":[113],"outperforms":[114],"state-of-the-art":[115],"baselines":[116],"various":[118],"real-world":[119],"datasets.":[121]},"counts_by_year":[{"year":2026,"cited_by_count":296},{"year":2025,"cited_by_count":812},{"year":2024,"cited_by_count":691},{"year":2023,"cited_by_count":559},{"year":2022,"cited_by_count":420},{"year":2021,"cited_by_count":272},{"year":2020,"cited_by_count":170},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":6}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2017-09-25T00:00:00"}
