{"id":"https://openalex.org/W2771989524","doi":"https://doi.org/10.1145/3148055.3148069","title":"Second-Order Destination Inference using Semi-Supervised Self-Training for Entry-Only Passenger Data","display_name":"Second-Order Destination Inference using Semi-Supervised Self-Training for Entry-Only Passenger Data","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2771989524","doi":"https://doi.org/10.1145/3148055.3148069","mag":"2771989524"},"language":"en","primary_location":{"id":"doi:10.1145/3148055.3148069","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3148055.3148069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","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/A5031139851","display_name":"Rongye Shi","orcid":"https://orcid.org/0000-0003-4298-9358"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongye Shi","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089056623","display_name":"Peter Steenkiste","orcid":"https://orcid.org/0000-0001-7079-8212"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Steenkiste","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088276691","display_name":"Manuela Veloso","orcid":"https://orcid.org/0000-0001-6738-238X"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manuela Veloso","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.3124,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90341238,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"255","last_page":"264"},"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":0.9922999739646912,"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":0.9857000112533569,"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/unavailability","display_name":"Unavailability","score":0.7758383750915527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7741159200668335},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5982913374900818},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5621638298034668},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5299690365791321},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.48318782448768616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46587085723876953},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4483731985092163},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.42503973841667175},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4141005873680115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3874467611312866},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1425572633743286},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11005797982215881}],"concepts":[{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.7758383750915527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7741159200668335},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5982913374900818},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5621638298034668},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5299690365791321},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.48318782448768616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46587085723876953},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4483731985092163},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.42503973841667175},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4141005873680115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3874467611312866},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1425572633743286},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11005797982215881},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3148055.3148069","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3148055.3148069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.800000011920929,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320319180","display_name":"Carnegie Mellon Portugal","ror":null},{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1576117597","https://openalex.org/W1838129148","https://openalex.org/W1968817125","https://openalex.org/W1982300822","https://openalex.org/W1988580225","https://openalex.org/W2007373465","https://openalex.org/W2011869198","https://openalex.org/W2016790006","https://openalex.org/W2026524503","https://openalex.org/W2035971144","https://openalex.org/W2040356073","https://openalex.org/W2044985623","https://openalex.org/W2070751343","https://openalex.org/W2076663670","https://openalex.org/W2104554956","https://openalex.org/W2127551220","https://openalex.org/W2136504847","https://openalex.org/W2153892689","https://openalex.org/W2156937801","https://openalex.org/W2172211955","https://openalex.org/W2271614222","https://openalex.org/W2295167361","https://openalex.org/W2364336568","https://openalex.org/W2599552708","https://openalex.org/W2617955281","https://openalex.org/W2912306800","https://openalex.org/W6735318697","https://openalex.org/W6738623367"],"related_works":["https://openalex.org/W4237235066","https://openalex.org/W2026539069","https://openalex.org/W207884067","https://openalex.org/W3127016596","https://openalex.org/W2365973415","https://openalex.org/W3146085540","https://openalex.org/W1482423459","https://openalex.org/W2996457675","https://openalex.org/W2129918226","https://openalex.org/W843992174"],"abstract_inverted_index":{"Automated":[0,172],"data":[1,20,38,123,154,176],"collection":[2],"in":[3,35,162],"urban":[4],"transportation":[5],"systems":[6],"produces":[7],"a":[8,16,40,76,95,108,117,156],"large":[9],"volume":[10],"of":[11,18,32,58,94,107,116,165],"passenger":[12,28,37,48],"data.":[13],"However,":[14],"quite":[15],"few":[17],"the":[19,25,59,65,72,86,92,100,105,114,130,136,145,163,169,179,200],"are":[21],"still":[22],"incomplete,":[23],"limiting":[24],"insight":[26],"into":[27],"mobility.":[29],"The":[30,89,182],"unavailability":[31],"destination":[33],"information":[34],"entry-only":[36],"is":[39],"very":[41],"common":[42],"issue.":[43],"Traditional":[44],"approaches":[45],"for":[46,71],"estimating":[47],"destinations":[49,102],"rely":[50],"on":[51,104],"heuristics":[52],"that":[53,186,191],"can":[54],"recover":[55],"only":[56],"some":[57],"missing":[60,87,101],"destinations.":[61,88],"To":[62,133],"deal":[63],"with":[64,124],"remaining":[66],"incomplete":[67],"data,":[68],"this":[69],"paper,":[70],"first":[73],"time,":[74],"proposes":[75],"second-order":[77],"inference":[78,201],"methodology":[79,90],"to":[80,84,98,120,128,143,188],"leverage":[81],"semi-supervised":[82],"self-training":[83],"infer":[85],"involves":[91],"design":[93,115],"base":[96,146],"learner":[97],"predict":[99],"based":[103],"statistics":[106],"selected":[109],"similarity-based":[110],"\"training":[111],"set\",":[112],"and":[113,168,203],"selection":[118],"strategy":[119],"select":[121],"new":[122],"high":[125,206],"prediction":[126],"confidence":[127],"update":[129],"training":[131],"set.":[132],"further":[134],"improve":[135],"inference,":[137],"we":[138],"incorporate":[139],"personal":[140],"history":[141],"priors":[142],"modify":[144],"learner.":[147],"We":[148],"evaluate":[149],"our":[150,196],"designs":[151],"using":[152],"two":[153],"sources:":[155],"real-data":[157],"inspired":[158],"traffic-passenger":[159],"behavior":[160],"simulation":[161],"city":[164],"Porto,":[166],"Portugal,":[167],"real":[170],"bus":[171],"Fare":[173],"Collection":[174],"(AFC)":[175],"collected":[177],"from":[178],"same":[180],"city.":[181],"experimental":[183],"results":[184],"show":[185],"compared":[187],"baseline":[189],"methods":[190],"do":[192],"not":[193],"use":[194],"self-training,":[195],"approach":[197],"significantly":[198],"improves":[199],"performance":[202],"achieves":[204],"notably":[205],"accuracies.":[207]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}