{"id":"https://openalex.org/W7130367786","doi":"https://doi.org/10.48550/arxiv.2602.15270","title":"Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models","display_name":"Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models","publication_year":2026,"publication_date":"2026-02-17","ids":{"openalex":"https://openalex.org/W7130367786","doi":"https://doi.org/10.48550/arxiv.2602.15270"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15270","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126345699","display_name":"Farbod Abbasi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbasi, Farbod","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126282229","display_name":"Zachary Patterson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patterson, Zachary","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123769991","display_name":"Bilal Farooq","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farooq, Bilal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.17919999361038208,"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":0.17919999361038208,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.11800000071525574,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.08869999647140503,"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/regularization","display_name":"Regularization (linguistics)","score":0.5001000165939331},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4885999858379364},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.4625999927520752},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.450300008058548},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4390999972820282},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.438400000333786},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.41620001196861267},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.41100001335144043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6398000121116638},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49880000948905945},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46639999747276306},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.4625999927520752},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.450300008058548},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4390999972820282},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.41100001335144043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39079999923706055},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.367900013923645},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3635999858379364},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3402999937534332},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27459999918937683}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15270","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15270","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8298168778419495}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generating":[0],"realistic":[1],"synthetic":[2,113,223],"populations":[3,224],"is":[4],"essential":[5],"for":[6,123,137,218,230],"agent-based":[7],"models":[8],"(ABM)":[9],"in":[10,185,189,193],"transportation":[11],"and":[12,34,60,73,88,110,139,146,151,171,182,191,209,244],"urban":[13],"planning.":[14],"Current":[15],"methods":[16],"face":[17],"two":[18],"major":[19],"limitations.":[20],"First,":[21],"many":[22],"rely":[23],"on":[24,143],"a":[25,30,82,93,117,133,186,200,211,227],"single":[26],"dataset":[27],"or":[28],"follow":[29],"sequential":[31,164,220],"data":[32,114],"fusion":[33],"generation":[35],"process,":[36],"which":[37,69],"means":[38],"they":[39],"fail":[40],"to":[41,66,85,216,239],"capture":[42],"the":[43,71,76,108,124,129,152,158,163,176,219,237,242],"complex":[44],"interplay":[45],"between":[46],"features.":[47],"Second,":[48],"these":[49],"approaches":[50],"struggle":[51],"with":[52,99,166],"sampling":[53],"zeros":[54,62],"(valid":[55],"but":[56],"unobserved":[57],"attribute":[58],"combinations)":[59],"structural":[61],"(infeasible":[63],"combinations":[64],"due":[65],"logical":[67],"constraints),":[68],"reduce":[70],"diversity":[72,109,145,181],"feasibility":[74,111,147],"of":[75,112,213,246],"generated":[77],"data.":[78],"This":[79,102],"study":[80],"proposes":[81],"novel":[83],"method":[84,105],"simultaneously":[86],"integrate":[87],"synthesize":[89],"multi-source":[90,233],"datasets":[91],"using":[92,199],"Wasserstein":[94],"Generative":[95],"Adversarial":[96],"Network":[97],"(WGAN)":[98],"gradient":[100,121],"penalty.":[101],"joint":[103,160,204],"learning":[104],"improves":[106,180],"both":[107],"by":[115,169,173],"defining":[116],"regularization":[118,177],"term":[119,178],"(inverse":[120],"penalty)":[122],"generator":[125],"loss":[126],"function.":[127],"For":[128],"evaluation,":[130],"we":[131],"implement":[132],"unified":[134],"evaluation":[135],"metric":[136],"similarity,":[138],"place":[140],"special":[141],"emphasis":[142],"measuring":[144],"through":[148],"recall,":[149],"precision,":[150],"F1":[153],"score.":[154,202],"Results":[155],"show":[156],"that":[157],"proposed":[159],"approach":[161,205,235],"outperforms":[162],"baseline,":[165],"recall":[167,190],"increasing":[168],"7\\%":[170],"precision":[172],"15\\%.":[174],"Additionally,":[175],"further":[179],"feasibility,":[183],"reflected":[184],"10\\%":[187],"increase":[188],"1\\%":[192],"precision.":[194],"We":[195],"assess":[196],"similarity":[197],"distributions":[198],"five-metric":[201],"The":[203],"performs":[206],"better":[207],"overall,":[208],"reaches":[210],"score":[212],"88.1":[214],"compared":[215],"84.6":[217],"method.":[221],"Since":[222],"serve":[225],"as":[226],"key":[228],"input":[229],"ABM,":[231],"this":[232],"generative":[234],"has":[236],"potential":[238],"significantly":[240],"enhance":[241],"accuracy":[243],"reliability":[245],"ABM.":[247]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-02-19T00:00:00"}