{"id":"https://openalex.org/W4385811608","doi":"https://doi.org/10.3390/ijgi12080338","title":"Profiling Public Transit Passenger Mobility Using Adversarial Learning","display_name":"Profiling Public Transit Passenger Mobility Using Adversarial Learning","publication_year":2023,"publication_date":"2023-08-12","ids":{"openalex":"https://openalex.org/W4385811608","doi":"https://doi.org/10.3390/ijgi12080338"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi12080338","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi12080338","pdf_url":"https://www.mdpi.com/2220-9964/12/8/338/pdf?version=1692000197","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/2220-9964/12/8/338/pdf?version=1692000197","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066767445","display_name":"Yicong Li","orcid":"https://orcid.org/0009-0007-1533-2234"},"institutions":[{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yicong Li","raw_affiliation_strings":["Zhongnan Engineering Corporation Limited, Power China, Changsha 410014, China"],"raw_orcid":"https://orcid.org/0009-0007-1533-2234","affiliations":[{"raw_affiliation_string":"Zhongnan Engineering Corporation Limited, Power China, Changsha 410014, China","institution_ids":["https://openalex.org/I4210120238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100378774","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0002-0683-4669"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tong Zhang","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"],"raw_orcid":"https://orcid.org/0000-0002-0683-4669","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071457014","display_name":"Xiaofei Lv","orcid":"https://orcid.org/0000-0002-1712-630X"},"institutions":[{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofei Lv","raw_affiliation_strings":["Zhongnan Engineering Corporation Limited, Power China, Changsha 410014, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhongnan Engineering Corporation Limited, Power China, Changsha 410014, China","institution_ids":["https://openalex.org/I4210120238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110235722","display_name":"Yingxi Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I39521962","display_name":"Hunan City University","ror":"https://ror.org/01vd7vb53","country_code":"CN","type":"education","lineage":["https://openalex.org/I39521962"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingxi Lu","raw_affiliation_strings":["College of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China","institution_ids":["https://openalex.org/I39521962"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021495990","display_name":"Wangshu Wang","orcid":"https://orcid.org/0000-0003-2307-155X"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Wangshu Wang","raw_affiliation_strings":["Department of Geodesy and Geoinformation, TU Wien, A-1040 Vienna, Austria"],"raw_orcid":"https://orcid.org/0000-0003-2307-155X","affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geoinformation, TU Wien, A-1040 Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100378774"],"corresponding_institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.736,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.78694944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"12","issue":"8","first_page":"338","last_page":"338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9950000047683716,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9825000166893005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.7456259727478027},{"id":"https://openalex.org/keywords/smart-card","display_name":"Smart card","score":0.7348784804344177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6524338722229004},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.48416024446487427},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47944799065589905},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4757528007030487},{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.46147701144218445},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4606018662452698},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4327571392059326},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41597995162010193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3540000319480896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33765554428100586},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.33356767892837524},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.19635868072509766},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19217431545257568},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1293092966079712}],"concepts":[{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.7456259727478027},{"id":"https://openalex.org/C110406131","wikidata":"https://www.wikidata.org/wiki/Q41349","display_name":"Smart card","level":2,"score":0.7348784804344177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6524338722229004},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.48416024446487427},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47944799065589905},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4757528007030487},{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.46147701144218445},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4606018662452698},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4327571392059326},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41597995162010193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3540000319480896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33765554428100586},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.33356767892837524},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.19635868072509766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19217431545257568},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1293092966079712},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/ijgi12080338","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi12080338","pdf_url":"https://www.mdpi.com/2220-9964/12/8/338/pdf?version=1692000197","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:d019cb0b7abe4447abc88c7598f85c02","is_oa":true,"landing_page_url":"https://doaj.org/article/d019cb0b7abe4447abc88c7598f85c02","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 12, Iss 8, p 338 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2220-9964/12/8/338/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/ijgi12080338","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information; Volume 12; Issue 8; Pages: 338","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/ijgi12080338","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi12080338","pdf_url":"https://www.mdpi.com/2220-9964/12/8/338/pdf?version=1692000197","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":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324116","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385811608.pdf"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1651166699","https://openalex.org/W1972309850","https://openalex.org/W1982300822","https://openalex.org/W1987228002","https://openalex.org/W2010612397","https://openalex.org/W2019720014","https://openalex.org/W2037629065","https://openalex.org/W2044985623","https://openalex.org/W2055195882","https://openalex.org/W2056284729","https://openalex.org/W2090978188","https://openalex.org/W2134268609","https://openalex.org/W2142338861","https://openalex.org/W2318951588","https://openalex.org/W2520241474","https://openalex.org/W2560603808","https://openalex.org/W2734775449","https://openalex.org/W2741206673","https://openalex.org/W2760942449","https://openalex.org/W2808113502","https://openalex.org/W2862340573","https://openalex.org/W2903645654","https://openalex.org/W2904403013","https://openalex.org/W2911662370","https://openalex.org/W2919292274","https://openalex.org/W2942023622","https://openalex.org/W2949704773","https://openalex.org/W2998574808","https://openalex.org/W3001357734","https://openalex.org/W3034912136","https://openalex.org/W3080501557","https://openalex.org/W3136455710","https://openalex.org/W3184219015","https://openalex.org/W3217016897","https://openalex.org/W4294170691","https://openalex.org/W4308424096","https://openalex.org/W4308799120","https://openalex.org/W4309651822","https://openalex.org/W4310434537","https://openalex.org/W4311519526","https://openalex.org/W4321064245","https://openalex.org/W6682691769","https://openalex.org/W6753408501","https://openalex.org/W6757059376"],"related_works":["https://openalex.org/W2141099407","https://openalex.org/W1560871288","https://openalex.org/W1570365136","https://openalex.org/W4319999113","https://openalex.org/W261978425","https://openalex.org/W2581781998","https://openalex.org/W1507665494","https://openalex.org/W2051044998","https://openalex.org/W1653295266","https://openalex.org/W2371308268"],"abstract_inverted_index":{"It":[0],"is":[1,76,101,174],"important":[2],"to":[3,12,51,81,103,124,136],"capture":[4],"passengers\u2019":[5],"public":[6,31,60,112,138,169],"transit":[7,61,84,113,118,139,170,212,224],"behavior":[8,58],"and":[9,25,36,55,69,110,155,161,183,196,206,229],"their":[10],"mobility":[11,57,85,119,140,225],"create":[13],"profiles,":[14],"which":[15,151],"are":[16],"critical":[17],"for":[18],"analyzing":[19],"human":[20],"activities,":[21],"understanding":[22],"the":[23,53,177,209,218],"social":[24],"economic":[26],"structure":[27],"of":[28,59,87,141,148,158,168,202,208],"cities,":[29],"improving":[30],"transportation,":[32],"assisting":[33],"urban":[34],"planning,":[35],"promoting":[37],"smart":[38,66,190],"cities.":[39],"In":[40],"this":[41,181],"paper,":[42,182],"we":[43,115],"develop":[44,131],"a":[45,92,126,132,153,162,227],"generative":[46,133],"adversarial":[47,134],"machine":[48],"learning":[49],"network":[50,71,135,194],"characterize":[52],"temporal":[54],"spatial":[56],"passengers,":[62,114],"based":[63,90,188],"on":[64,91,189],"massive":[65],"card":[67,191],"data":[68],"road":[70,193],"data.":[72,199],"The":[73,143],"Apriori":[74],"algorithm":[75],"extended":[77],"with":[78],"spatio-temporal":[79,122],"constraints":[80,123],"extract":[82],"frequent":[83,111],"patterns":[86],"individual":[88],"passengers":[89],"reconstructed":[93],"personal":[94],"trip":[95],"dataset.":[96],"This":[97],"individual-level":[98],"pattern":[99],"information":[100],"used":[102],"construct":[104,125],"personalized":[105],"feature":[106,128],"vectors.":[107],"For":[108],"regular":[109],"identify":[116],"similar":[117],"groups":[120],"using":[121],"group":[127],"vector.":[129],"We":[130],"embed":[137],"passengers.":[142,171],"proposed":[144,219],"model\u2019s":[145],"generator":[146],"consists":[147],"an":[149],"auto-encoder,":[150],"extracts":[152],"low-dimensional":[154],"compact":[156,230],"representation":[157,205],"passenger":[159,223],"behavior,":[160],"pre-trained":[163],"sub-generator":[164],"containing":[165],"generalization":[166],"features":[167],"Shenzhen":[172],"City":[173],"taken":[175],"as":[176],"study":[178],"area":[179],"in":[180,226],"experiments":[184],"were":[185,214],"carried":[186],"out":[187],"data,":[192,195],"bus":[197],"GPS":[198],"Clustering":[200],"analysis":[201],"embedding":[203],"vector":[204],"estimation":[207],"top":[210],"K":[211],"destinations":[213],"conducted,":[215],"verifying":[216],"that":[217],"method":[220],"can":[221],"profile":[222],"comprehensive":[228],"manner.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}