{"id":"https://openalex.org/W4205500134","doi":"https://doi.org/10.1109/bigdata52589.2021.9671682","title":"Co-Prediction of Multimodal Transportation Demands With Self-learned Spatial Dependence","display_name":"Co-Prediction of Multimodal Transportation Demands With Self-learned Spatial Dependence","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205500134","doi":"https://doi.org/10.1109/bigdata52589.2021.9671682"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671682","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5103259414","display_name":"Mingzhe Liu","orcid":"https://orcid.org/0000-0003-2906-4289"},"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":"Mingzhe Liu","raw_affiliation_strings":["State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Software Development Environment, 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"]},{"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":"Bowen Du","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China","State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","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"]},{"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":"Leilei Sun","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China","State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4323,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.90995151,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"824","last_page":"833"},"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":0.9987999796867371,"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":0.7336719632148743},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6386535167694092},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5735267996788025},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5509200096130371},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.5280265212059021},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4366462230682373},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.4148557782173157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33193135261535645},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.25650539994239807},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16597941517829895},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10607215762138367},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07951876521110535},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.07921630144119263}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336719632148743},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6386535167694092},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5735267996788025},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5509200096130371},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5280265212059021},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4366462230682373},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.4148557782173157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33193135261535645},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.25650539994239807},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16597941517829895},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10607215762138367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07951876521110535},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.07921630144119263},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671682","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334926","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1988580225","https://openalex.org/W2165835468","https://openalex.org/W2295598076","https://openalex.org/W2575125657","https://openalex.org/W2613331518","https://openalex.org/W2756203131","https://openalex.org/W2770969945","https://openalex.org/W2772724270","https://openalex.org/W2775717462","https://openalex.org/W2782920454","https://openalex.org/W2788134583","https://openalex.org/W2788950488","https://openalex.org/W2807894308","https://openalex.org/W2884738862","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2914743966","https://openalex.org/W2942681667","https://openalex.org/W2963358464","https://openalex.org/W2964015378","https://openalex.org/W2965341826","https://openalex.org/W2998436408","https://openalex.org/W3001314220","https://openalex.org/W3012562343","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3094588037","https://openalex.org/W3103720336","https://openalex.org/W4244069611","https://openalex.org/W4288363255","https://openalex.org/W4297733535","https://openalex.org/W6726873649","https://openalex.org/W6746015598","https://openalex.org/W6773493362","https://openalex.org/W6780221082"],"related_works":["https://openalex.org/W1598955744","https://openalex.org/W4384111961","https://openalex.org/W4285167822","https://openalex.org/W2951710344","https://openalex.org/W2144602163","https://openalex.org/W2204136089","https://openalex.org/W2127985649","https://openalex.org/W4249202279","https://openalex.org/W1555391540","https://openalex.org/W4298161250"],"abstract_inverted_index":{"Transportation":[0],"demand":[1,26,63],"prediction":[2,21,178,182],"is":[3,30,105],"a":[4,24,31,57,69,90,101],"classic":[5],"problem":[6],"in":[7,23,176],"intelligent":[8],"transportation":[9,40,53,62,85,98,146,171,191],"research.":[10],"However,":[11],"most":[12],"exist":[13],"studies":[14],"have":[15,154],"been":[16],"focused":[17],"on":[18,122,151],"improving":[19],"the":[20,36,49,96,109,123,135,142,158,161,187],"accuracy":[22],"single":[25],"mode,":[27],"and":[28,55,83,87,115],"there":[29],"lack":[32],"of":[33,35,38,51,80,95,111,138,144,160,169,189],"understanding":[34],"impact":[37],"multiple":[39,52,190],"modes.":[41],"To":[42],"this":[43],"paper,":[44],"we":[45,66,128],"aim":[46],"to":[47,107,118,133,141],"uncover":[48],"interactions":[50,188],"modes":[54,192],"develop":[56],"co-prediction":[58,168],"method":[59],"for":[60],"multimodal":[61,145,170],"prediction.":[64],"Specifically,":[65],"first":[67],"propose":[68],"self-learned":[70,124],"spatial":[71,78,92,125],"graph":[72,94],"construction":[73],"method,":[74,163],"which":[75],"automatically":[76],"learns":[77],"dependencies":[79],"both":[81],"homogeneous":[82],"heterogeneous":[84],"stations,":[86],"then":[88],"constructs":[89],"mode-free":[91],"dependence":[93],"studied":[97],"stations.":[99,147],"Then,":[100],"spatiotemporal":[102],"convolution":[103],"module":[104],"provided":[106],"update":[108],"state":[110,137],"each":[112,139],"station":[113,140],"spatially":[114],"temporally":[116],"according":[117],"its":[119],"neighbor":[120],"stations":[121],"graph.":[126],"Moreover,":[127],"design":[129],"an":[130],"output":[131],"layer":[132],"map":[134],"hidden":[136],"demands":[143,172],"Finally,":[148],"experimental":[149],"results":[150],"real-world":[152],"data":[153],"not":[155],"only":[156],"validated":[157],"effectiveness":[159],"proposed":[162],"but":[164],"also":[165],"revealed":[166],"that":[167],"could":[173],"always":[174],"result":[175],"higher":[177],"performances":[179],"than":[180],"single-mode":[181],"methods":[183],"as":[184],"it":[185],"takes":[186],"into":[193],"account.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
