{"id":"https://openalex.org/W4391769735","doi":"https://doi.org/10.1109/itsc57777.2023.10422310","title":"Enhancing Joint Behavior Modeling with Route Choice Using Adversarial Inverse Reinforcement Learning","display_name":"Enhancing Joint Behavior Modeling with Route Choice Using Adversarial Inverse Reinforcement Learning","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391769735","doi":"https://doi.org/10.1109/itsc57777.2023.10422310"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422310","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/A5066038537","display_name":"Daichi Ogawa","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Daichi Ogawa","raw_affiliation_strings":["University of Tokyo,Department of Civil Engineering,Tokyo,Japan","Department of Civil Engineering, University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Department of Civil Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Civil Engineering, University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061487176","display_name":"Eiji Hato","orcid":"https://orcid.org/0000-0003-3932-7791"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Eiji Hato","raw_affiliation_strings":["University of Tokyo,Department of Civil Engineering,Tokyo,Japan","Department of Civil Engineering, University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Department of Civil Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Civil Engineering, University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066038537"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.3579,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68917305,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4750","last_page":"4755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.4652000069618225,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.4652000069618225,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.44519999623298645,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.41119998693466187,"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/adversarial-system","display_name":"Adversarial system","score":0.8490383625030518},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7726302146911621},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6808827519416809},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.6480749249458313},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6292036175727844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49636775255203247},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.46944212913513184},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3433007001876831},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.14329540729522705},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14164578914642334},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13234427571296692}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8490383625030518},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7726302146911621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6808827519416809},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.6480749249458313},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6292036175727844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49636775255203247},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.46944212913513184},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3433007001876831},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.14329540729522705},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14164578914642334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13234427571296692},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10422310","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":[{"id":"https://metadata.un.org/sdg/10","score":0.5099999904632568,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1777239053","https://openalex.org/W1973039793","https://openalex.org/W1981678289","https://openalex.org/W2057094343","https://openalex.org/W2083194543","https://openalex.org/W2103498243","https://openalex.org/W2105767494","https://openalex.org/W2143856073","https://openalex.org/W2145067550","https://openalex.org/W2566467060","https://openalex.org/W2736601468","https://openalex.org/W2766772953","https://openalex.org/W2883057932","https://openalex.org/W2963277051","https://openalex.org/W2963508354","https://openalex.org/W2999905431","https://openalex.org/W3091146265","https://openalex.org/W4320013936","https://openalex.org/W4322485644","https://openalex.org/W6638088447","https://openalex.org/W6681342480","https://openalex.org/W6718092244","https://openalex.org/W6731259203","https://openalex.org/W6741002519","https://openalex.org/W6745347688","https://openalex.org/W6753207554","https://openalex.org/W6755963907","https://openalex.org/W6763248972","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Route":[0],"choice":[1,52,126,149],"modeling":[2,53],"is":[3,73,85,187],"an":[4],"important":[5],"issue":[6],"in":[7,78,123],"transportation":[8,121],"planning.":[9],"In":[10,64,142],"recent":[11],"years,":[12],"the":[13,40,47,57,90,97,100,107,117,128,135,140,153,160,172,192],"importance":[14],"of":[15,46,99,106,119,131,139,181,195],"comfortable":[16],"urban":[17,67],"spaces":[18],"not":[19],"only":[20],"for":[21,25],"vehicles":[22],"but":[23],"also":[24,188],"pedestrians":[26],"has":[27],"been":[28,36],"increasing":[29],"toward":[30],"low-carbon":[31],"society.":[32],"Accordingly,":[33],"efforts":[34],"have":[35],"made":[37],"to":[38,56,87,95,115,190],"model":[39,132,150],"pedestrians'":[41],"characteristic":[42],"behaviors.":[43],"However,":[44,110],"most":[45],"existing":[48],"studies":[49],"about":[50],"route":[51,125,148],"are":[54,112],"limited":[55,91,136],"representation":[58],"within":[59],"a":[60,65,79,104,124,147],"single":[61],"traffic":[62,71,176,196],"mode.":[63],"typical":[66],"street":[68],"space,":[69],"each":[70,76],"mode":[72],"interacting":[74],"with":[75],"other":[77],"complex":[80],"manner.":[81],"Describing":[82],"multimodal":[83],"interaction":[84,118,173],"necessary":[86],"efficiently":[88],"allocate":[89],"road":[92,101],"space":[93,102],"and":[94,134,167],"maximize":[96],"value":[98],"as":[103],"result":[105],"network":[108],"effect.":[109],"there":[111],"two":[113],"challenges":[114],"describing":[116],"multiple":[120,175],"modes":[122,177],"model:":[127],"computational":[129],"cost":[130],"estimation":[133],"expressive":[137,183],"power":[138],"model.":[141],"this":[143],"study,":[144],"we":[145],"construct":[146],"based":[151],"on":[152],"AIRL":[154],"method":[155],"which":[156],"can":[157,168],"rapidly":[158],"estimate":[159],"user":[161],"equilibrium":[162],"under":[163],"multi-agent":[164],"Markov":[165],"game":[166],"take":[169],"into":[170],"account":[171],"among":[174],"explicitly.":[178],"The":[179],"use":[180],"highly":[182],"machine":[184],"learning":[185],"models":[186],"expected":[189],"lead":[191],"high":[193],"reproducibility":[194],"behavior.":[197]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-04T09:10:02.777135","created_date":"2025-10-10T00:00:00"}
