{"id":"https://openalex.org/W4283770265","doi":"https://doi.org/10.1145/3534678.3539273","title":"Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction","display_name":"Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4283770265","doi":"https://doi.org/10.1145/3534678.3539273"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539273","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.15005","any_repository_has_fulltext":true},"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":true,"raw_author_name":"Liangzhe Han","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103191418","display_name":"Xiaojian Ma","orcid":"https://orcid.org/0009-0006-9842-1242"},"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":"Xiaojian Ma","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"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"]},{"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":["Beihang University & Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University & Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081507644","display_name":"Bowen Du","orcid":"https://orcid.org/0000-0002-3755-4870"},"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":"Bowen Du","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"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"],"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/A5109299440","display_name":"Weifeng Lv","orcid":null},"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":"Weifeng Lv","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, UNK, China"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, UNK, China","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5000608590"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":11.1266,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.99308943,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"516","last_page":"524"},"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":0.9979000091552734,"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":0.7768423557281494},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7271445989608765},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.690163791179657},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.6222708225250244},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5759099721908569},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.5436940789222717},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5415670275688171},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.48426908254623413},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47525927424430847},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4675086438655853},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4329023063182831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4255143105983734},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3728550672531128},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3624159097671509},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10974439978599548},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10180094838142395},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0929558277130127}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7768423557281494},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7271445989608765},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.690163791179657},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.6222708225250244},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5759099721908569},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.5436940789222717},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5415670275688171},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.48426908254623413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47525927424430847},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4675086438655853},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4329023063182831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4255143105983734},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3728550672531128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3624159097671509},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10974439978599548},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10180094838142395},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0929558277130127},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3534678.3539273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539273","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2206.15005","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.15005","pdf_url":"https://arxiv.org/pdf/2206.15005","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-121061","is_oa":false,"landing_page_url":"http://www.scopus.com/record/display.url?eid=2-s2.0-85137142545&origin=inward","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.15005","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.15005","pdf_url":"https://arxiv.org/pdf/2206.15005","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2747329762","https://openalex.org/W2756203131","https://openalex.org/W2798918712","https://openalex.org/W2806983170","https://openalex.org/W2904832339","https://openalex.org/W2907192581","https://openalex.org/W2922924332","https://openalex.org/W2945726694","https://openalex.org/W2946160394","https://openalex.org/W2949732208","https://openalex.org/W2956452632","https://openalex.org/W2962711740","https://openalex.org/W2963358464","https://openalex.org/W2964015378","https://openalex.org/W2965683718","https://openalex.org/W2972093239","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W3007404067","https://openalex.org/W3029978369","https://openalex.org/W3031609778","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3080902222","https://openalex.org/W3094231942","https://openalex.org/W3101358844","https://openalex.org/W3101588560","https://openalex.org/W3103720336","https://openalex.org/W3108897154","https://openalex.org/W3119249947","https://openalex.org/W3119688269","https://openalex.org/W3161012769","https://openalex.org/W4285723986","https://openalex.org/W4287755062","https://openalex.org/W4294558607"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2369897927","https://openalex.org/W3031731056","https://openalex.org/W4293167957","https://openalex.org/W2361035307","https://openalex.org/W2380455807","https://openalex.org/W2993975634","https://openalex.org/W2367835030"],"abstract_inverted_index":{"Traffic":[0],"demand":[1,23,73],"forecasting":[2],"by":[3],"deep":[4],"neural":[5],"networks":[6],"has":[7],"attracted":[8],"widespread":[9],"interest":[10],"in":[11],"both":[12],"academia":[13],"and":[14,48,64,108,150,154,169,189,193],"industry":[15],"society.":[16],"Among":[17],"them,":[18],"the":[19,36,56,114,129,175,194,197,203],"pairwise":[20],"Origin-Destination":[21,72],"(OD)":[22],"prediction":[24,74],"is":[25,84,125,163],"a":[26,62,77,88,120,159],"valuable":[27],"but":[28,146],"challenging":[29],"problem":[30],"due":[31],"to":[32,113,127,165],"several":[33],"factors:":[34],"(i)":[35],"large":[37],"number":[38],"of":[39,45,51,132,199],"possible":[40],"OD":[41],"pairs,":[42],"(ii)":[43],"implicitness":[44],"spatial":[46,130],"dependence,":[47],"(iii)":[49],"complexity":[50],"traffic":[52,94],"states.":[53],"To":[54],"address":[55],"above":[57],"issues,":[58],"this":[59],"paper":[60],"proposes":[61],"Continuous-time":[63],"Multi-level":[65],"dynamic":[66,79,89],"graph":[67,80],"representation":[68,81],"learning":[69,82,123],"method":[70],"for":[71,92,174],"(CMOD).":[75],"Firstly,":[76],"continuous-time":[78],"framework":[83],"constructed,":[85],"which":[86],"maintains":[87],"state":[90,102],"vector":[91],"each":[93],"node":[95,172],"(metro":[96],"stations":[97],"or":[98],"taxi":[99],"zones).":[100],"The":[101],"vectors":[103],"keep":[104],"historical":[105],"transaction":[106],"information":[107],"are":[109,180],"continuously":[110],"updated":[111],"according":[112],"most":[115],"recently":[116],"happened":[117],"transactions.":[118],"Secondly,":[119],"multi-level":[121,167],"structure":[122],"module":[124,162],"proposed":[126],"model":[128,201],"dependency":[131],"station-level":[133],"nodes.":[134,157],"It":[135],"can":[136],"not":[137],"only":[138],"exploit":[139],"relations":[140],"between":[141],"nodes":[142],"adaptively":[143],"from":[144,186],"data,":[145],"also":[147],"share":[148],"messages":[149],"representations":[151,173],"via":[152],"cluster-level":[153],"area-level":[155],"virtual":[156],"Lastly,":[158],"cross-level":[160],"fusion":[161],"designed":[164],"integrate":[166],"memories":[168],"generate":[170],"comprehensive":[171],"final":[176],"prediction.":[177],"Extensive":[178],"experiments":[179],"conducted":[181],"on":[182],"two":[183],"real-world":[184],"datasets":[185],"Beijing":[187],"Subway":[188],"New":[190],"York":[191],"Taxi,":[192],"results":[195],"demonstrate":[196],"superiority":[198],"our":[200],"against":[202],"state-of-the-art":[204],"approaches.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":5}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}