{"id":"https://openalex.org/W4417501191","doi":"https://doi.org/10.3390/ijgi15010002","title":"Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction","display_name":"Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction","publication_year":2025,"publication_date":"2025-12-19","ids":{"openalex":"https://openalex.org/W4417501191","doi":"https://doi.org/10.3390/ijgi15010002"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi15010002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010002","pdf_url":"https://www.mdpi.com/2220-9964/15/1/2/pdf?version=1766146226","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/15/1/2/pdf?version=1766146226","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hongxiang Li","orcid":"https://orcid.org/0009-0001-8573-4892"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxiang Li","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":"https://orcid.org/0009-0001-8573-4892","affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000921508","display_name":"Zhiming Gui","orcid":"https://orcid.org/0000-0002-9677-4152"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Gui","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":"https://orcid.org/0000-0002-9677-4152","affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027351570","display_name":"Zhenji Gao","orcid":"https://orcid.org/0009-0007-9106-039X"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenji Gao","raw_affiliation_strings":["Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China","Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China"],"raw_orcid":"https://orcid.org/0009-0007-9106-039X","affiliations":[{"raw_affiliation_string":"Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China","institution_ids":["https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027351570"],"corresponding_institution_ids":["https://openalex.org/I211433327","https://openalex.org/I2799486974"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39477144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"1","first_page":"2","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9775999784469604,"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":0.9775999784469604,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.006899999920278788,"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":0.006800000090152025,"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/feature","display_name":"Feature (linguistics)","score":0.5867000222206116},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5295000076293945},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4893999993801117},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.46889999508857727},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4194999933242798},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.37709999084472656},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3732999861240387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7229999899864197},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5867000222206116},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4050000011920929},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.37709999084472656},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2759999930858612}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/ijgi15010002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010002","pdf_url":"https://www.mdpi.com/2220-9964/15/1/2/pdf?version=1766146226","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7f121b6613d74ef9a21b4de2716e4a75","is_oa":true,"landing_page_url":"https://doaj.org/article/7f121b6613d74ef9a21b4de2716e4a75","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":"ISPRS International Journal of Geo-Information, Vol 15, Iss 1, p 2 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/ijgi15010002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010002","pdf_url":"https://www.mdpi.com/2220-9964/15/1/2/pdf?version=1766146226","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8447194009","display_name":null,"funder_award_id":"U2344216","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417501191.pdf","grobid_xml":"https://content.openalex.org/works/W4417501191.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W1964357740","https://openalex.org/W2756203131","https://openalex.org/W2901504064","https://openalex.org/W2965341826","https://openalex.org/W4312933868","https://openalex.org/W4367359628","https://openalex.org/W4387806972"],"related_works":[],"abstract_inverted_index":{"Origin\u2013destination":[0],"(OD)":[1],"flow":[2,79],"prediction":[3,165,184],"is":[4,206],"fundamental":[5],"to":[6,92,114],"intelligent":[7,190],"transportation":[8,191,197],"systems,":[9,192],"yet":[10],"existing":[11],"diffusion-based":[12],"models":[13],"face":[14],"two":[15],"critical":[16,171],"limitations.":[17],"First,":[18],"they":[19],"inadequately":[20],"exploit":[21],"spatial":[22,86],"semantics,":[23],"focusing":[24],"primarily":[25],"on":[26,120],"temporal":[27],"dependencies":[28],"or":[29,199],"topological":[30],"correlations":[31],"while":[32,148],"neglecting":[33],"urban":[34],"functional":[35,200],"heterogeneity":[36],"encoded":[37],"in":[38,58],"Points":[39],"of":[40,142,173],"Interest":[41],"(POIs).":[42],"Second,":[43],"static":[44],"embedding":[45],"fusion":[46],"cannot":[47],"dynamically":[48],"capture":[49,115],"semantic":[50,87,174,204],"importance":[51],"variations":[52],"during":[53,55],"denoising\u2014particularly":[54],"traffic":[56],"surges":[57],"POI-dense":[59],"areas.":[60],"To":[61],"address":[62],"these":[63],"gaps,":[64],"we":[65],"propose":[66],"the":[67,111,133,139,153,170],"Cross-Attention":[68],"Diffusion":[69],"Model":[70],"(CADM),":[71],"a":[72],"semantically":[73],"conditioned":[74],"framework":[75],"for":[76,182,189,194],"short-term":[77],"OD":[78],"forecasting.":[80],"CADM":[81,125,137],"integrates":[82],"POI":[83,162],"embeddings":[84],"as":[85],"priors":[88],"and":[89,185],"employs":[90],"cross-attention":[91],"enable":[93],"semantic-guided":[94],"denoising,":[95],"facilitating":[96],"dynamic":[97],"spatiotemporal":[98,183],"feature":[99],"fusion.":[100],"This":[101],"design":[102],"adaptively":[103],"reweights":[104],"regional":[105],"representations":[106],"throughout":[107],"reverse":[108],"diffusion,":[109],"enhancing":[110],"model\u2019s":[112],"capacity":[113],"complex":[116],"mobility":[117],"patterns.":[118],"Experiments":[119],"real-world":[121],"datasets":[122],"demonstrate":[123],"that":[124,160],"achieves":[126],"balanced":[127],"performance":[128,151],"across":[129],"multiple":[130],"metrics.":[131],"At":[132],"30":[134],"min":[135,155],"horizon,":[136],"attains":[138],"lowest":[140],"RMSE":[141],"5.77,":[143],"outperforming":[144],"iTransformer":[145],"by":[146,167],"1.9%,":[147],"maintaining":[149],"competitive":[150],"at":[152],"15":[154],"horizon.":[156],"Ablation":[157],"studies":[158],"confirm":[159],"removing":[161],"features":[163],"increases":[164],"errors":[166],"15\u201320%,":[168],"validating":[169],"role":[172],"conditioning.":[175],"These":[176],"findings":[177],"advance":[178],"semantic-aware":[179],"generative":[180],"modeling":[181],"provide":[186],"practical":[187],"insights":[188],"particularly":[193],"newly":[195],"established":[196],"hubs":[198],"zone":[201],"reconfigurations":[202],"where":[203],"understanding":[205],"essential.":[207]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-12-19T00:00:00"}
