{"id":"https://openalex.org/W3012943949","doi":"https://doi.org/10.1145/3366423.3380235","title":"Inferring Passengers\u2019 Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning","display_name":"Inferring Passengers\u2019 Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012943949","doi":"https://doi.org/10.1145/3366423.3380235","mag":"3012943949"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380235","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380235","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380235","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062085075","display_name":"Mingzhou Yang","orcid":"https://orcid.org/0000-0002-8354-4622"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingzhou Yang","raw_affiliation_strings":["Xi'an Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630059","display_name":"Yanhua Li","orcid":"https://orcid.org/0000-0001-8972-503X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanhua Li","raw_affiliation_strings":["Worcester Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198510","display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["The University of Iowa"],"affiliations":[{"raw_affiliation_string":"The University of Iowa","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101486498","display_name":"Hui Lu","orcid":"https://orcid.org/0000-0002-4120-7716"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Lu","raw_affiliation_strings":["Guangzhou University"],"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056608045","display_name":"Zhihong Tian","orcid":"https://orcid.org/0000-0002-9409-5359"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihong Tian","raw_affiliation_strings":["Guangzhou University"],"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106731907","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-2032-0381"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Lenovo Research Hong Kong"],"affiliations":[{"raw_affiliation_string":"Lenovo Research Hong Kong","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5062085075"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.4026,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.62558923,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1637","last_page":"1647"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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":0.9998000264167786,"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":0.9980999827384949,"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/computer-science","display_name":"Computer science","score":0.7004009485244751},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5958377718925476},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5848267674446106},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5346537828445435},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.5175389647483826},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.4835216999053955},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.45593684911727905},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40978604555130005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28120991587638855},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14528274536132812},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.11715227365493774},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1110231876373291}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7004009485244751},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5958377718925476},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5848267674446106},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5346537828445435},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.5175389647483826},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.4835216999053955},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.45593684911727905},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40978604555130005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28120991587638855},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14528274536132812},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.11715227365493774},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1110231876373291},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380235","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380235","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380235","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380235","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1496590343","https://openalex.org/W1513468570","https://openalex.org/W1550669880","https://openalex.org/W1576452626","https://openalex.org/W1963649406","https://openalex.org/W1999874108","https://openalex.org/W2003257780","https://openalex.org/W2061562262","https://openalex.org/W2067484689","https://openalex.org/W2089953722","https://openalex.org/W2098774185","https://openalex.org/W2112738128","https://openalex.org/W2114140676","https://openalex.org/W2121863487","https://openalex.org/W2132339352","https://openalex.org/W2147544021","https://openalex.org/W2164637474","https://openalex.org/W2296704245","https://openalex.org/W2330024298","https://openalex.org/W2339514589","https://openalex.org/W2397891592","https://openalex.org/W2398344594","https://openalex.org/W2404673495","https://openalex.org/W2604998144","https://openalex.org/W2624735749","https://openalex.org/W2775800859","https://openalex.org/W2779551119","https://openalex.org/W2944211469","https://openalex.org/W2963368198","https://openalex.org/W3019591413","https://openalex.org/W4234761190"],"related_works":["https://openalex.org/W3096874164","https://openalex.org/W2937181779","https://openalex.org/W2386410636","https://openalex.org/W1985560493","https://openalex.org/W2357975469","https://openalex.org/W2145363145","https://openalex.org/W1626977535","https://openalex.org/W2341346307","https://openalex.org/W3168977894","https://openalex.org/W187740018"],"abstract_inverted_index":{"Public":[0],"transports,":[1],"such":[2],"as":[3,61,170],"subway":[4],"lines":[5],"and":[6,12,46,183,239,256],"buses,":[7],"offer":[8],"affordable":[9],"ride-sharing":[10],"services":[11],"reduce":[13],"the":[14,71,86,105,127,130,136,144,157,160,173,176,179,180,184,200,211,218,240,247,272,282,286,291,312,319,324,327,338],"road":[15],"network":[16],"traffic.":[17],"Extracting":[18],"passengers\u2019":[19,54],"preferences":[20,77],"from":[21,104],"their":[22,47,94],"public":[23,38],"transit":[24,44,80],"choices":[25,81],"is":[26,148,333],"important":[27],"to":[28,74,100,118,129,155,165,171,217,271,298,318,335],"city":[29],"planners":[30],"but":[31],"technically":[32],"non-trivial.":[33],"When":[34],"traveling":[35],"by":[36,52,234,330],"taking":[37],"transits,":[39],"passengers":[40,89],"make":[41,70],"sequences":[42],"of":[43,78,111,138,159,175,187,225,281,288,301,311,326,337],"choices,":[45],"rewards":[48],"are":[49],"usually":[50,90],"influenced":[51],"other":[53],"choices.":[55],"This":[56,190],"process":[57],"can":[58,277,307],"be":[59],"modeled":[60],"a":[62,115,149,193,223,252,257,299],"Markov":[63],"Game":[64,255],"(MG).":[65],"In":[66,153,290],"this":[67],"paper,":[68],"we":[69,96,123,125,162,208,214,268,294],"first":[72],"effort":[73],"model":[75],"travelers\u2019":[76,120],"making":[79],"using":[82],"MGs.":[83],"Based":[84],"on":[85,250],"discovery":[87],"that":[88,266,336],"do":[91],"not":[92],"change":[93],"policies,":[95],"propose":[97,229],"novel":[98],"algorithms":[99,238,249],"extract":[101],"reward":[102,140,167,283,322],"functions":[103,141,168],"observed":[106,181],"deterministic":[107],"equilibrium":[108,151,219],"joint":[109,132,146,220],"policy":[110,147,182,186,221,328],"all":[112,139],"agents":[113],"in":[114,192],"general-sum":[116],"MG":[117],"infer":[119],"preferences.":[121],"First,":[122],"assume":[124],"have":[126,215,269,296],"access":[128,216,270,297],"entire":[131],"policy.":[133,152,340],"We":[134,228,245],"characterize":[135],"set":[137,224,300],"for":[142,199],"which":[143],"given":[145],"Nash":[150],"order":[154],"remove":[156],"degeneracy":[158],"solution,":[161],"then":[163],"attempt":[164],"pick":[166],"so":[169],"maximize":[172],"sum":[174],"deviation":[177],"between":[178],"sub-optimal":[185],"each":[188],"agent.":[189],"results":[191],"skillfully":[194],"solvable":[195],"linear":[196],"programming":[197],"algorithm":[198,232,276,306,332],"multi-agent":[201],"inverse":[202],"reinforcement":[203],"learning":[204,237],"(MA-IRL)":[205],"problem.":[206],"Then,":[207],"deal":[209],"with":[210,316],"case":[212,292],"where":[213,293],"through":[222],"actual":[226],"trajectories.":[227,314],"an":[230,309],"iterative":[231],"inspired":[233],"single-agent":[235],"apprenticeship":[236],"cyclic":[241],"coordinate":[242],"descent":[243],"approach.":[244],"evaluate":[246],"proposed":[248],"both":[251],"simple":[253],"Grid":[254],"unique":[258],"real-world":[259],"dataset":[260],"(from":[261],"Shenzhen,":[262],"China).":[263],"Results":[264],"show":[265],"when":[267],"full":[273],"policy,":[274],"our":[275,305,331],"efficiently":[278],"recover":[279],"most":[280],"structure,":[284],"especially":[285],"interaction":[287],"agents.":[289],"only":[295],"sampled":[302],"expert":[303,313,339],"trajectories,":[304],"provide":[308],"explanation":[310],"Measured":[315],"respect":[317],"experts\u2019":[320],"unknown":[321],"function,":[323],"performance":[325],"output":[329],"close":[334]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
