{"id":"https://openalex.org/W4392472402","doi":"https://doi.org/10.3390/sym16030308","title":"Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction","display_name":"Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction","publication_year":2024,"publication_date":"2024-03-05","ids":{"openalex":"https://openalex.org/W4392472402","doi":"https://doi.org/10.3390/sym16030308"},"language":"en","primary_location":{"id":"doi:10.3390/sym16030308","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030308","pdf_url":"https://www.mdpi.com/2073-8994/16/3/308/pdf?version=1709638803","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/16/3/308/pdf?version=1709638803","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106614043","display_name":"Chenyang Cao","orcid":"https://orcid.org/0009-0005-3540-1669"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Cao","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061702931","display_name":"Yinxin Bao","orcid":"https://orcid.org/0000-0002-8830-0678"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinxin Bao","raw_affiliation_strings":["School of Information Science and Technology, Nantong University, Nantong 226019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Nantong University, Nantong 226019, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040398133","display_name":"Quan Shi","orcid":"https://orcid.org/0000-0003-0877-5989"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Quan Shi","raw_affiliation_strings":["School of Information Science and Technology, Nantong University, Nantong 226019, China","School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Nantong University, Nantong 226019, China","institution_ids":["https://openalex.org/I199305430"]},{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008965695","display_name":"Qin-Qin Shen","orcid":"https://orcid.org/0000-0002-2795-8409"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qinqin Shen","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China","institution_ids":["https://openalex.org/I199305430"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5008965695","https://openalex.org/A5040398133"],"corresponding_institution_ids":["https://openalex.org/I199305430"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":1.9841,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84083274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"16","issue":"3","first_page":"308","last_page":"308"},"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":0.9943000078201294,"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":0.9940000176429749,"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":0.7553794384002686},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6176628470420837},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5743789076805115},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5615982413291931},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5595614910125732},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.46293705701828003},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4411683976650238},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.42368948459625244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41480809450149536},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41187185049057007},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3828449249267578},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27740854024887085},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14821290969848633},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11779782176017761}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7553794384002686},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6176628470420837},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5743789076805115},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5615982413291931},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5595614910125732},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.46293705701828003},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4411683976650238},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.42368948459625244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41480809450149536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41187185049057007},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3828449249267578},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27740854024887085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14821290969848633},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11779782176017761},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/sym16030308","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030308","pdf_url":"https://www.mdpi.com/2073-8994/16/3/308/pdf?version=1709638803","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a93b16eab3124062985377d6b8429e47","is_oa":false,"landing_page_url":"https://doaj.org/article/a93b16eab3124062985377d6b8429e47","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 16, Iss 3, p 308 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/sym16030308","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030308","pdf_url":"https://www.mdpi.com/2073-8994/16/3/308/pdf?version=1709638803","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G1918312737","display_name":null,"funder_award_id":"2022044","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7980617406","display_name":null,"funder_award_id":"KYCX23_3396","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8306646943","display_name":null,"funder_award_id":"KYCX22_3341","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G980063043","display_name":"\u9762\u5411\u6d41\u91cf\u9884\u6d4b\u7684\u4ea4\u901a\u667a\u8111\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61771265","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/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4392472402.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2190353863","https://openalex.org/W2695427614","https://openalex.org/W2756203131","https://openalex.org/W2792440155","https://openalex.org/W2889230014","https://openalex.org/W2891280833","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2962825546","https://openalex.org/W2965341826","https://openalex.org/W2972370624","https://openalex.org/W2996847713","https://openalex.org/W3000301417","https://openalex.org/W3035911592","https://openalex.org/W3082919371","https://openalex.org/W3103720336","https://openalex.org/W3175925542","https://openalex.org/W3176075655","https://openalex.org/W3210027742","https://openalex.org/W3211407228","https://openalex.org/W3212041461","https://openalex.org/W4200125053","https://openalex.org/W4200619887","https://openalex.org/W4213015446","https://openalex.org/W4221032654","https://openalex.org/W4225858632","https://openalex.org/W4288447614","https://openalex.org/W4293745133","https://openalex.org/W4303980415","https://openalex.org/W4306316920","https://openalex.org/W4307296199","https://openalex.org/W4312703862","https://openalex.org/W4313201657","https://openalex.org/W4315837307","https://openalex.org/W4323314209","https://openalex.org/W4328103753","https://openalex.org/W4361275072","https://openalex.org/W4367298393","https://openalex.org/W4367311960","https://openalex.org/W4379472313","https://openalex.org/W4385597355"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W1988032185","https://openalex.org/W2898021863","https://openalex.org/W4320029439","https://openalex.org/W2521335480","https://openalex.org/W3121692546","https://openalex.org/W2611370603"],"abstract_inverted_index":{"Accurate":[0],"and":[1,12,53,70,116,162],"real-time":[2],"traffic":[3,75,95,151,173],"speed":[4,96],"prediction":[5,190],"remains":[6],"challenging":[7],"due":[8],"to":[9,30,37,65,129,144,166],"the":[10,39,45,55,67,74,79,104,110,117,125,131,146,168,185],"irregularity":[11],"asymmetry":[13],"of":[14,41,49,57,73,103,150,172,180],"real-traffic":[15],"road":[16],"networks.":[17],"Existing":[18],"models":[19],"based":[20],"on":[21,157,177],"graph":[22,28,90,160],"convolutional":[23,91],"networks":[24],"commonly":[25],"use":[26],"multi-layer":[27],"convolution":[29,161],"extract":[31],"an":[32],"undirected":[33],"static":[34],"adjacency":[35],"matrix":[36],"map":[38],"correlation":[40,50],"nodes,":[42],"which":[43],"ignores":[44],"dynamic":[46,132],"symmetry":[47],"change":[48],"over":[51],"time":[52],"faces":[54],"challenge":[56],"oversmoothing":[58],"during":[59],"training":[60],"iterations,":[61],"making":[62],"it":[63],"difficult":[64],"learn":[66,145,167],"spatial":[68,170],"structure":[69,143],"temporal":[71,148],"trend":[72],"network.":[76],"To":[77],"overcome":[78],"above":[80],"challenges,":[81],"we":[82],"propose":[83],"a":[84,141,178],"novel":[85],"multi-head":[86],"self-attention":[87],"gated":[88,163],"spatiotemporal":[89,133],"network":[92],"(MSGSGCN)":[93],"for":[94],"prediction.":[97],"The":[98,137,153],"MSGSGCN":[99,187],"model":[100,188],"mainly":[101],"consists":[102],"Node":[105],"Correlation":[106],"Estimator":[107],"(NCE)":[108],"module,":[109,115],"Time":[111],"Residual":[112],"Learner":[113],"(TRL)":[114],"Gated":[118],"Graph":[119],"Convolutional":[120],"Fusion":[121],"(GGCF)":[122],"module.":[123],"Specifically,":[124],"NCE":[126],"module":[127,139,155],"aims":[128],"capture":[130],"correlations":[134],"between":[135],"nodes.":[136],"TRL":[138],"utilizes":[140],"residual":[142],"long-term":[147],"features":[149,171],"data.":[152,174],"GGCF":[154],"relies":[156],"adaptive":[158],"diffusion":[159],"recurrent":[164],"units":[165],"key":[169],"Experimental":[175],"analysis":[176],"pair":[179],"real-world":[181],"datasets":[182],"indicates":[183],"that":[184],"proposed":[186],"enhances":[189],"accuracy":[191],"by":[192],"more":[193],"than":[194],"4%":[195],"when":[196],"contrasted":[197],"with":[198],"state-of-the-art":[199],"models.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
