{"id":"https://openalex.org/W3080911033","doi":"https://doi.org/10.1145/3394486.3403358","title":"Hybrid Spatio-Temporal Graph Convolutional Network","display_name":"Hybrid Spatio-Temporal Graph Convolutional Network","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080911033","doi":"https://doi.org/10.1145/3394486.3403358","mag":"3080911033"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403358","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403358","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403358","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108295919","display_name":"Rui Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rui Dai","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016572127","display_name":"Shenkun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenkun Xu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100989844","display_name":"Qian Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Gu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016185897","display_name":"Chenguang Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenguang Ji","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007772432","display_name":"Kaikui Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaikui Liu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108295919"],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":9.1038,"has_fulltext":true,"cited_by_count":104,"citation_normalized_percentile":{"value":0.98519996,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3074","last_page":"3082"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9993000030517578,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9983000159263611,"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":0.7747353315353394},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5182116627693176},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5052106976509094},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.42349714040756226},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.4153628647327423},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3363472521305084},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24010491371154785}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7747353315353394},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5182116627693176},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5052106976509094},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.42349714040756226},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.4153628647327423},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3363472521305084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24010491371154785}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403358","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403358","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403358","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403358","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403358","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3080911033.pdf","grobid_xml":"https://content.openalex.org/works/W3080911033.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W139044672","https://openalex.org/W2036785686","https://openalex.org/W2070232376","https://openalex.org/W2108196201","https://openalex.org/W2145527836","https://openalex.org/W2153154015","https://openalex.org/W2166771065","https://openalex.org/W2460404912","https://openalex.org/W2468907370","https://openalex.org/W2496357561","https://openalex.org/W2756203131","https://openalex.org/W2809148419","https://openalex.org/W2809366716","https://openalex.org/W2965399951","https://openalex.org/W2968911474","https://openalex.org/W3103720336","https://openalex.org/W4244319607"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2153421018","https://openalex.org/W2117632582"],"abstract_inverted_index":{"Traffic":[0],"forecasting":[1,28],"has":[2],"recently":[3],"attracted":[4],"increasing":[5],"interest":[6],"due":[7],"to":[8,21,55,73,119],"the":[9,22,34,45,62,75,87,96,110,121,133,158],"popularity":[10],"of":[11,25,36,64,86,160],"online":[12,81],"navigation":[13,82],"services,":[14],"ridesharing":[15],"and":[16,114],"smart":[17],"city":[18],"projects.":[19],"Owing":[20],"non-stationary":[23],"nature":[24],"road":[26],"traffic,":[27],"accuracy":[29],"is":[30,53],"fundamentally":[31],"limited":[32],"by":[33,60],"lack":[35],"contextual":[37],"information.":[38],"To":[39],"address":[40],"this":[41,107],"issue,":[42],"we":[43,69,125],"propose":[44,70],"Hybrid":[46],"Spatio-Temporal":[47],"Graph":[48],"Convolutional":[49],"Network":[50],"(H-STGCN),":[51],"which":[52,131],"able":[54],"\"deduce\"":[56],"future":[57],"travel":[58,103],"time":[59],"exploiting":[61],"data":[63],"upcoming":[65,76,97],"traffic":[66,77,135],"volume.":[67],"Specifically,":[68],"an":[71,80],"algorithm":[72],"acquire":[74],"volume":[78,98],"from":[79],"engine.":[83],"Taking":[84],"advantage":[85],"piecewise-linear":[88],"flow-density":[89],"relationship,":[90],"a":[91,127],"novel":[92],"transformer":[93],"structure":[94],"converts":[95],"into":[99],"its":[100],"equivalent":[101],"in":[102,153],"time.":[104],"We":[105,137],"combine":[106],"signal":[108],"with":[109],"commonly-utilized":[111],"travel-time":[112],"signal,":[113],"then":[115],"apply":[116],"graph":[117],"convolution":[118],"capture":[120],"spatial":[122],"dependency.":[123],"Particularly,":[124],"construct":[126],"compound":[128],"adjacency":[129],"matrix":[130],"reflects":[132],"innate":[134],"proximity.":[136],"conduct":[138],"extensive":[139],"experiments":[140],"on":[141],"real-world":[142],"datasets.":[143],"The":[144],"results":[145],"show":[146],"that":[147],"H-STGCN":[148],"remarkably":[149],"outperforms":[150],"state-of-the-art":[151],"methods":[152],"various":[154],"metrics,":[155],"especially":[156],"for":[157],"prediction":[159],"non-recurring":[161],"congestion.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":16}],"updated_date":"2026-03-29T08:15:47.926485","created_date":"2020-09-01T00:00:00"}