{"id":"https://openalex.org/W4391793345","doi":"https://doi.org/10.1109/itsc57777.2023.10422234","title":"Generating Realistic Synthetic Traffic Data using Conditional Tabular Generative Adversarial Networks for Intelligent Transportation Systems","display_name":"Generating Realistic Synthetic Traffic Data using Conditional Tabular Generative Adversarial Networks for Intelligent Transportation Systems","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391793345","doi":"https://doi.org/10.1109/itsc57777.2023.10422234"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"article","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/A5019511504","display_name":"Archana Nigam","orcid":"https://orcid.org/0000-0003-0962-4365"},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Archana Nigam","raw_affiliation_strings":["School of Computing, Kaushalya - The Skill University,Ahmedabad,India","School of Computing, Kaushalya - The Skill University, Ahmedabad, India"],"affiliations":[{"raw_affiliation_string":"School of Computing, Kaushalya - The Skill University,Ahmedabad,India","institution_ids":["https://openalex.org/I52527943"]},{"raw_affiliation_string":"School of Computing, Kaushalya - The Skill University, Ahmedabad, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101606091","display_name":"Sanjay Srivastava","orcid":"https://orcid.org/0000-0003-1306-5076"},"institutions":[{"id":"https://openalex.org/I98389781","display_name":"Dhirubhai Ambani Institute of Information and Communication Technology","ror":"https://ror.org/02d5b7g69","country_code":"IN","type":"education","lineage":["https://openalex.org/I98389781"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay Srivastava","raw_affiliation_strings":["ICT at DAIICT,Gandhinagar,India","ICT at DAIICT, Gandhinagar, India"],"affiliations":[{"raw_affiliation_string":"ICT at DAIICT,Gandhinagar,India","institution_ids":["https://openalex.org/I98389781"]},{"raw_affiliation_string":"ICT at DAIICT, Gandhinagar, India","institution_ids":["https://openalex.org/I98389781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019511504"],"corresponding_institution_ids":["https://openalex.org/I52527943"],"apc_list":null,"apc_paid":null,"fwci":0.6304,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67802381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2881","last_page":"2886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9901999831199646,"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":0.9901999831199646,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9283999800682068,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9246000051498413,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7282278537750244},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6473117470741272},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6085752248764038},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.513293445110321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46758484840393066},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.43209943175315857},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42746812105178833},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.22234410047531128},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.2151973843574524},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16082099080085754},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.12648740410804749}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282278537750244},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6473117470741272},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6085752248764038},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.513293445110321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46758484840393066},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.43209943175315857},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42746812105178833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22234410047531128},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.2151973843574524},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16082099080085754},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.12648740410804749}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1529430712","https://openalex.org/W1536719639","https://openalex.org/W1565946331","https://openalex.org/W1969428227","https://openalex.org/W2022998827","https://openalex.org/W2083238230","https://openalex.org/W2096863518","https://openalex.org/W2111688141","https://openalex.org/W2117284672","https://openalex.org/W2125389028","https://openalex.org/W2475569566","https://openalex.org/W2529827714","https://openalex.org/W2783478760","https://openalex.org/W2897256107","https://openalex.org/W2941466173","https://openalex.org/W3037624214","https://openalex.org/W3040508529","https://openalex.org/W3096831136","https://openalex.org/W3167297489","https://openalex.org/W3198781016","https://openalex.org/W3213716934","https://openalex.org/W4288296172","https://openalex.org/W4308810895","https://openalex.org/W4360973825","https://openalex.org/W6678815747","https://openalex.org/W6759423363","https://openalex.org/W6765451912","https://openalex.org/W6780640948"],"related_works":["https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W3178813832","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2969215546","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4226298148"],"abstract_inverted_index":{"Intelligent":[0],"transportation":[1,21],"systems":[2,193],"(ITS)":[3],"are":[4,81],"a":[5,82,231],"set":[6],"of":[7,20,24,34,47,85,110,130,157,173,177,233,255],"technologies":[8],"that":[9,89,95,159,239,260],"can":[10],"be":[11],"used":[12,185],"to":[13,64,137,210],"improve":[14],"the":[15,25,35,44,55,97,103,108,119,146,152,155,161,167,171,175,208,252],"efficiency,":[16],"safety,":[17],"and":[18,49,99,112,125,163,194,204,213,237,244],"sustainability":[19],"systems.":[22],"One":[23,33],"key":[26],"challenges":[27],"in":[28,41,91,102,107,123,151,166,186,200,224],"ITS":[29,42,51,124,201],"is":[30,43,179,202],"data":[31,39,56,94,120],"sparsity.":[32],"main":[36],"reasons":[37],"for":[38,219],"sparsity":[40,57,121],"high":[45],"cost":[46],"maintaining":[48],"deploying":[50],"infrastructure.":[52],"To":[53],"overcome":[54],"is-sue,":[58],"researchers":[59],"have":[60,183],"proposed":[61],"different":[62],"approaches":[63],"generate":[65,138,211],"synthetic":[66,93,139,246,257],"traffic":[67,72,140,196,235,247],"data,":[68,105,215,258],"such":[69,189],"as":[70,190],"employing":[71],"simulators":[73],"or":[74],"statistical":[75],"methods.":[76],"Generative":[77,133],"Adversarial":[78,134],"Networks":[79,135],"(GANs)":[80],"powerful":[83],"class":[84],"deep":[86],"learning":[87],"models":[88],"excel":[90],"generating":[92],"exhibits":[96,262],"characteristics":[98],"found":[100],"patterns":[101,164],"training":[104],"particularly":[106],"domains":[109,188],"images":[111],"text.":[113],"In":[114,170],"this":[115],"paper,":[116],"we":[117,250],"discuss":[118],"challenge":[122],"present":[126],"our":[127,228,240,256],"pioneering":[128],"use":[129],"Conditional":[131],"Tabular":[132],"(CTGANs)":[136],"data.":[141,169,248,267],"It":[142],"takes":[143],"into":[144],"account":[145],"conditional":[147],"dependencies":[148],"among":[149],"columns":[150],"dataset,":[153],"enabling":[154],"generation":[156],"samples":[158],"respect":[160],"relationships":[162],"observed":[165],"real":[168],"field":[172],"ITS,":[174],"application":[176],"CTGANs":[178,182,206],"unexplored.":[180],"While":[181],"been":[184],"other":[187],"intrusion":[191],"detection":[192],"network":[195],"analysis,":[197],"their":[198],"utilization":[199],"novel":[203],"unprecedented.":[205],"offer":[207],"potential":[209],"fine-grained":[212],"realistic":[214,243],"making":[216],"them":[217],"valuable":[218],"creating":[220],"accurate":[221,245],"simulation":[222],"setups":[223],"ITS.":[225],"We":[226],"evaluate":[227],"approach":[229],"using":[230],"range":[232],"real-world":[234],"datasets":[236],"demonstrate":[238],"method":[241],"produces":[242],"Additionally,":[249],"assess":[251],"generalization":[253],"capability":[254],"finding":[259],"it":[261],"strong":[263],"performance":[264],"on":[265],"unseen":[266]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}