{"id":"https://openalex.org/W4399601399","doi":"https://doi.org/10.1109/access.2024.3413852","title":"Capturing Spatial-Temporal Traffic Patterns: A Dynamic Partitioning Strategy for Heterogeneous Traffic Networks","display_name":"Capturing Spatial-Temporal Traffic Patterns: A Dynamic Partitioning Strategy for Heterogeneous Traffic Networks","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399601399","doi":"https://doi.org/10.1109/access.2024.3413852"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3413852","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3413852","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3413852","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xianyue Peng","orcid":"https://orcid.org/0009-0001-2063-2748"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyue Peng","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0001-2063-2748","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012720408","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0001-7961-7588"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-7961-7588","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6427,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63149267,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"131982","last_page":"131992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9593999981880188,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.7728930711746216},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.4112946689128876},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.37157994508743286},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.15516245365142822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728930711746216},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.4112946689128876},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.37157994508743286},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.15516245365142822},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3413852","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3413852","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d1cf4a099ae14a3998d87b171368e51d","is_oa":true,"landing_page_url":"https://doaj.org/article/d1cf4a099ae14a3998d87b171368e51d","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 131982-131992 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3413852","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3413852","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2124025480","display_name":null,"funder_award_id":"KYCX23_0298","funder_id":"https://openalex.org/F4320327786","funder_display_name":"Major Technology Innovation Projects of Jiangsu Province"},{"id":"https://openalex.org/G5633075461","display_name":null,"funder_award_id":"2022ZD0115600","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"},{"id":"https://openalex.org/G8378238378","display_name":null,"funder_award_id":"52072067","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/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320327786","display_name":"Major Technology Innovation Projects of Jiangsu Province","ror":null},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1980295590","https://openalex.org/W2027062751","https://openalex.org/W2086376232","https://openalex.org/W2086662759","https://openalex.org/W2121947440","https://openalex.org/W2132914434","https://openalex.org/W2160884799","https://openalex.org/W2164863800","https://openalex.org/W2409967033","https://openalex.org/W2723368897","https://openalex.org/W2745719948","https://openalex.org/W2749594142","https://openalex.org/W2770189438","https://openalex.org/W3040951648","https://openalex.org/W3172335600","https://openalex.org/W4250123324","https://openalex.org/W6689637345"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Macroscopic":[0],"fundamental":[1],"diagram":[2],"(MFD)":[3],"has":[4],"become":[5],"a":[6,16,47,58,80,165],"popular":[7],"model":[8],"used":[9],"in":[10,110,145],"developing":[11],"network":[12,49,75,102,148,153,170],"traffic":[13,19,54,69],"controls":[14],"for":[15,52,169],"roughly":[17],"homogeneous":[18],"network,":[20],"encounters":[21],"limitations":[22],"when":[23],"applied":[24],"to":[25,62,98,135,176,181],"the":[26,65,73,90,99,115,122,137,143,146,151],"inherently":[27],"heterogeneous":[28,53,152],"nature":[29],"of":[30,89,117,124,142],"real-world":[31],"transportation":[32],"networks,":[33],"affecting":[34],"its":[35],"predictive":[36],"accuracy":[37],"and":[38,101,139,149,178],"applicability.":[39],"To":[40,71],"address":[41],"these":[42],"challenges,":[43],"this":[44],"paper":[45],"proposes":[46],"dynamic":[48,74,126],"partitioning":[50,76,91,127,171],"strategy":[51],"networks.":[55],"We":[56],"devise":[57],"spatial-temporal":[59,68],"dual-form":[60],"graph":[61],"accurately":[63],"represent":[64],"road":[66,147],"network\u2019s":[67],"patterns.":[70],"solve":[72],"problem,":[77],"we":[78],"employ":[79],"spectral":[81],"theory":[82],"technique":[83],"known":[84],"as":[85,104,172],"RatioCut.":[86],"Our":[87],"evaluation":[88],"methods\u2019":[92],"effectiveness":[93],"relies":[94],"on":[95],"fitting":[96],"performance":[97,123],"MFD":[100],"modularity":[103],"metrics.":[105],"A":[106],"case":[107],"study":[108],"set":[109],"Yangzhou,":[111],"China,":[112],"executed":[113],"with":[114],"Simulation":[116],"Urban":[118],"Mobility":[119],"(SUMO),":[120],"demonstrates":[121],"our":[125,132],"approach.":[128],"The":[129],"results":[130],"highlight":[131],"method\u2019s":[133],"ability":[134],"capture":[136],"spatial":[138],"temporal":[140],"evolution":[141],"congestion":[144],"cluster":[150],"into":[154],"multiple":[155],"quasi-homogeneous":[156],"regions.":[157],"Moreover,":[158],"it":[159,173],"shows":[160],"that":[161],"speed":[162],"is":[163,179],"potentially":[164],"more":[166],"accessible":[167],"indicator":[168],"performs":[174],"similar":[175],"density":[177],"easier":[180],"collect.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}