{"id":"https://openalex.org/W3171958173","doi":"https://doi.org/10.1145/3447548.3467275","title":"Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting","display_name":"Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3171958173","doi":"https://doi.org/10.1145/3447548.3467275","mag":"3171958173"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467275","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5000608590","display_name":"Liangzhe Han","orcid":"https://orcid.org/0000-0002-1989-8231"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangzhe Han","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053487836","display_name":"Bowen Du","orcid":"https://orcid.org/0000-0003-0975-2367"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Du","raw_affiliation_strings":["Beihang Univeristy &amp; Peng Cheng Laboratory, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang Univeristy &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081275566","display_name":"Leilei Sun","orcid":"https://orcid.org/0000-0002-0157-1716"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leilei Sun","raw_affiliation_strings":["Beihang Univerisity &amp; Peng Cheng Laboratory, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang Univerisity &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032187620","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0002-1767-8024"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076992681","display_name":"Yisheng Lv","orcid":"https://orcid.org/0000-0002-7565-4979"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yisheng Lv","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["the State University of New Jersey, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the State University of New Jersey, Newark, NJ, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":262,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"547","last_page":"555"},"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":0.9969000220298767,"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/T10524","display_name":"Traffic control and management","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7823235392570496},{"id":"https://openalex.org/keywords/traffic-speed","display_name":"Traffic speed","score":0.6428685188293457},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.6029290556907654},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5211134552955627},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5194358825683594},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.47713494300842285},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4281502664089203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4240746796131134},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.4230012893676758},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.41770100593566895},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19672778248786926},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1813117265701294},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.14949074387550354},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09851235151290894},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09061497449874878}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7823235392570496},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.6428685188293457},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.6029290556907654},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5211134552955627},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5194358825683594},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.47713494300842285},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4281502664089203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4240746796131134},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.4230012893676758},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.41770100593566895},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19672778248786926},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1813117265701294},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.14949074387550354},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09851235151290894},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09061497449874878},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467275","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W1963826206","https://openalex.org/W2004353783","https://openalex.org/W2036785686","https://openalex.org/W2064675550","https://openalex.org/W2295598076","https://openalex.org/W2528639018","https://openalex.org/W2586139886","https://openalex.org/W2733312032","https://openalex.org/W2756203131","https://openalex.org/W2788114581","https://openalex.org/W2788134583","https://openalex.org/W2798058877","https://openalex.org/W2809366716","https://openalex.org/W2884738862","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2911662370","https://openalex.org/W2915117209","https://openalex.org/W2949382160","https://openalex.org/W2952734551","https://openalex.org/W2952740813","https://openalex.org/W2962756421","https://openalex.org/W2962790412","https://openalex.org/W2962890638","https://openalex.org/W2964933785","https://openalex.org/W2965341826","https://openalex.org/W2990955039","https://openalex.org/W2997848713","https://openalex.org/W3015379812","https://openalex.org/W3080252065","https://openalex.org/W3080253043","https://openalex.org/W3102476541","https://openalex.org/W3103720336","https://openalex.org/W3106462682"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2982430984","https://openalex.org/W2059018062","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2564285047","https://openalex.org/W4280593160","https://openalex.org/W2604585036","https://openalex.org/W2117632582"],"abstract_inverted_index":{"Dynamic":[0],"Graph":[1],"Neural":[2],"Networks":[3],"(DGNNs)":[4],"have":[5],"become":[6],"one":[7],"of":[8,52,113,134,149,217],"the":[9,50,64,73,111,130,159,172,181,203,210],"most":[10],"promising":[11],"methods":[12],"for":[13,21,108,115],"traffic":[14,22,66,75,86,106,117,178,187],"speed":[15,23,67,118],"forecasting.":[16,119],"However,":[17],"when":[18],"adapting":[19],"DGNNs":[20,114],"forecasting,":[24],"existing":[25],"approaches":[26],"are":[27],"usually":[28],"built":[29],"on":[30,158,192],"a":[31,61,123,138,164],"static":[32],"adjacency":[33,161],"matrix":[34],"(no":[35],"matter":[36],"predefined":[37],"or":[38],"self-learned)":[39],"to":[40,96,128,145,152,170],"learn":[41,129],"spatial":[42,132,215],"relationships":[43,216],"among":[44],"different":[45],"road":[46,54,135,218],"segments,":[47],"even":[48],"if":[49],"impact":[51],"two":[53],"segments":[55],"can":[56,199],"be":[57,70,79],"changeable":[58],"dynamically":[59],"during":[60],"day.":[62],"Moreover,":[63,163],"future":[65],"cannot":[68],"only":[69,201],"related":[71],"with":[72,180],"current":[74],"speed,":[76],"but":[77,207],"also":[78,208],"affected":[80],"by":[81,155],"other":[82],"factors":[83],"such":[84],"as":[85],"volumes.":[87],"To":[88],"this":[89,92],"end,":[90],"in":[91,105],"paper,":[93],"we":[94,121],"aim":[95],"explore":[97],"these":[98],"dynamic":[99,124,139,160,214],"and":[100,212],"multi-faceted":[101,165],"spatio-temporal":[102],"characteristics":[103],"inherent":[104],"data":[107,194],"further":[109],"unleashing":[110],"power":[112],"better":[116],"Specifically,":[120],"design":[122],"graph":[125,140],"construction":[126],"method":[127,198],"time-specific":[131],"dependencies":[133],"segments.":[136,219],"Then,":[137],"convolution":[141],"module":[142,167],"is":[143,168],"proposed":[144],"aggregate":[146],"hidden":[147,174,183],"states":[148,175,184],"neighbor":[150],"nodes":[151,154],"focal":[153],"message":[156],"passing":[157],"matrices.":[162],"fusion":[166],"provided":[169],"incorporate":[171],"auxiliary":[173],"learned":[176,185],"from":[177,186],"volumes":[179],"primary":[182],"speeds.":[188],"Finally,":[189],"experimental":[190],"results":[191],"real-world":[193],"demonstrate":[195],"that":[196],"our":[197],"not":[200],"achieve":[202],"state-of-the-art":[204],"prediction":[205],"performances,":[206],"obtain":[209],"explicit":[211],"interpretable":[213]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":78},{"year":2024,"cited_by_count":73},{"year":2023,"cited_by_count":72},{"year":2022,"cited_by_count":21}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
