{"id":"https://openalex.org/W7134971256","doi":"https://doi.org/10.1109/access.2026.3672633","title":"Bus OD Identification From Incomplete Smart Card Data: A Two-Layer Heterogeneous Decision Tree Approach","display_name":"Bus OD Identification From Incomplete Smart Card Data: A Two-Layer Heterogeneous Decision Tree Approach","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7134971256","doi":"https://doi.org/10.1109/access.2026.3672633"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3672633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3672633","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128697441","display_name":"Li Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Tang","raw_affiliation_strings":["School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128698923","display_name":"Jiayang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayang Li","raw_affiliation_strings":["School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0009-0007-8357-0218","affiliations":[{"raw_affiliation_string":"School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122895081","display_name":"Jiacheng Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiacheng Yin","raw_affiliation_strings":["School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0000-0002-9823-2461","affiliations":[{"raw_affiliation_string":"School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103226450","display_name":"Yuxiang Hu","orcid":"https://orcid.org/0000-0002-9711-6047"},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingying Hu","raw_affiliation_strings":["School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I102345215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102345215"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24034383,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"41103","last_page":"41118"},"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":0.8263999819755554,"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":0.8263999819755554,"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":0.12049999833106995,"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":0.025800000876188278,"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/public-transport","display_name":"Public transport","score":0.7077999711036682},{"id":"https://openalex.org/keywords/smart-card","display_name":"Smart card","score":0.6116999983787537},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.572700023651123},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5616000294685364},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4683000147342682},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.44609999656677246},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44110000133514404},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712000012397766},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.7077999711036682},{"id":"https://openalex.org/C110406131","wikidata":"https://www.wikidata.org/wiki/Q41349","display_name":"Smart card","level":2,"score":0.6116999983787537},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5936999917030334},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.572700023651123},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3792000114917755},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3125999867916107},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3672633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8d8f25933ee443b8b74b2c0c0c814e52","is_oa":true,"landing_page_url":"https://doaj.org/article/8d8f25933ee443b8b74b2c0c0c814e52","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":"IEEE Access, Vol 14, Pp 41103-41118 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3672633","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3672633","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6913900375366211}],"awards":[{"id":"https://openalex.org/G5560768199","display_name":null,"funder_award_id":"2023NSFSC0385","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"},{"id":"https://openalex.org/G64274424","display_name":null,"funder_award_id":"52102384","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"},{"id":"https://openalex.org/F4320329861","display_name":"Natural Science Foundation of Sichuan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"bus":[1,28,170],"Origin-Destination":[2],"(OD)":[3],"identification":[4,30,45,172],"based":[5],"on":[6,34],"public":[7,99,182],"transit":[8,100,183],"data":[9,101],"is":[10],"essential":[11],"for":[12,26,70,77,150,168],"route":[13],"planning":[14],"and":[15,57,73,145,165],"service":[16],"optimization.":[17],"However,":[18],"current":[19],"research":[20,161],"lacks":[21],"evaluation":[22],"metrics":[23],"specifically":[24],"designed":[25],"the":[27,42,106,116,169],"OD":[29,171],"task,":[31],"often":[32],"relying":[33],"conventional":[35],"indicators":[36],"that":[37,65,105],"fail":[38],"to":[39,158,179],"fully":[40],"capture":[41],"plausibility":[43],"of":[44],"results.":[46],"This":[47,160],"study":[48,97],"proposes":[49],"an":[50,147],"integrated":[51],"framework":[52],"encompassing":[53],"label":[54],"generation,":[55],"modeling,":[56],"validation.":[58],"Specifically,":[59],"we":[60],"developed":[61],"a":[62,67,74,87,163,175],"two-layer":[63],"model":[64],"employs":[66],"Random":[68],"Forest":[69],"feature":[71],"selection":[72],"CatBoost":[75],"classifier":[76],"alighting":[78],"stop":[79],"identification.":[80],"To":[81],"enable":[82],"validation":[83,124],"without":[84],"ground-truth":[85],"labels,":[86],"novel":[88],"Trend":[89],"Consistency":[90],"Index":[91],"(TCI)":[92],"was":[93],"introduced.":[94],"A":[95],"case":[96],"using":[98],"from":[102],"Chengdu":[103],"demonstrates":[104],"proposed":[107],"method":[108],"outperforms":[109],"mainstream":[110],"algorithms":[111],"such":[112],"as":[113],"XGBoost,":[114],"achieving":[115],"highest":[117],"scores":[118],"across":[119],"all":[120],"key":[121],"metrics.":[122],"Multi-dimensional":[123],"further":[125],"confirmed":[126],"its":[127],"effectiveness,":[128],"showing":[129],"high":[130],"fidelity":[131],"in":[132],"reproducing":[133],"temporal":[134],"travel":[135],"patterns,":[136],"accurately":[137],"identifying":[138],"major":[139],"destination":[140],"clusters":[141],"through":[142],"spatial":[143],"heatmaps,":[144],"yielding":[146],"excellent":[148],"fit":[149],"trip":[151],"distribution":[152],"curves":[153],"(R<sup":[154],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[155],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[156],"up":[157],"0.8382).":[159],"provides":[162],"robust":[164],"practical":[166],"solution":[167],"problem,":[173],"offering":[174],"reliable":[176],"quantitative":[177],"foundation":[178],"advance":[180],"smart":[181],"management.":[184]},"counts_by_year":[],"updated_date":"2026-03-21T06:30:42.041108","created_date":"2026-03-12T00:00:00"}