{"id":"https://openalex.org/W4408700598","doi":"https://doi.org/10.1109/itsc58415.2024.10919525","title":"Incorporating Graph Neural Network into Route Choice Model in Road Network","display_name":"Incorporating Graph Neural Network into Route Choice Model in Road Network","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4408700598","doi":"https://doi.org/10.1109/itsc58415.2024.10919525"},"language":"en","primary_location":{"id":"doi:10.1109/itsc58415.2024.10919525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10919525","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th 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/A5116733589","display_name":"Ma Yuxun","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ma Yuxun","raw_affiliation_strings":["Tokyo Institute of Technology,Department of Civil and Environmental Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology,Department of Civil and Environmental Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5116733590","display_name":"Seo Toru","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seo Toru","raw_affiliation_strings":["Tokyo Institute of Technology,Department of Civil and Environmental Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology,Department of Civil and Environmental Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5116733589"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29812463,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"603","last_page":"608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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/T11106","display_name":"Data Management and Algorithms","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9976999759674072,"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":0.7340223789215088},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5425300598144531},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43636777997016907},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3662465810775757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3384013772010803},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2645728290081024}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7340223789215088},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5425300598144531},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43636777997016907},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3662465810775757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3384013772010803},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2645728290081024}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc58415.2024.10919525","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10919525","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:t2r2.star.titech.ac.jp:50717988","is_oa":false,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100926077","pdf_url":null,"source":{"id":"https://openalex.org/S4377196385","display_name":"Tokyo Tech Research Repository (Tokyo Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114531698","host_organization_name":"Tokyo Institute of Technology","host_organization_lineage":["https://openalex.org/I114531698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1973063495","https://openalex.org/W2057094343","https://openalex.org/W2128672185","https://openalex.org/W2808039392","https://openalex.org/W2901504064","https://openalex.org/W3004748517","https://openalex.org/W3022222974","https://openalex.org/W3034798881","https://openalex.org/W3083430313","https://openalex.org/W3127415418","https://openalex.org/W3136840803","https://openalex.org/W3138337643","https://openalex.org/W3152249529","https://openalex.org/W4206608377","https://openalex.org/W4285272621","https://openalex.org/W4289884426","https://openalex.org/W4322485644","https://openalex.org/W4328008037","https://openalex.org/W6776830747"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Route":[0],"choice":[1,14],"model":[2,69,73,100,110,119],"is":[3,31],"important":[4],"for":[5,38],"Intelligent":[6],"Transportation":[7],"Systems.":[8],"The":[9,26],"most":[10],"commonly":[11],"used":[12],"route":[13],"models":[15,19,30,44],"include":[16],"seminal":[17],"logit":[18,24,72,118,126],"and":[20,35,74,120,132],"their":[21,32],"extension":[22],"re-cursive":[23],"model.":[25,127],"advantage":[27,82],"of":[28,70,83,91],"these":[29],"interpretable":[33],"parameters":[34],"thus":[36],"useful":[37],"policy":[39],"making.":[40],"Recently,":[41],"more":[42],"accurate":[43],"have":[45],"been":[46],"proposed":[47,66,109],"by":[48,139],"using":[49],"data-driven":[50],"approaches":[51],"such":[52],"as":[53,86,88],"deep":[54],"neural":[55],"networks,":[56],"however,":[57],"they":[58],"generally":[59],"lacks":[60],"interpretabllity.":[61],"In":[62],"this":[63],"study,":[64],"we":[65],"a":[67,121],"hybrid":[68],"recursive":[71,117],"Directed":[75],"Graph":[76],"Neural":[77,123],"Network":[78],"which":[79],"has":[80],"the":[81,99,129,133,141],"interpletable":[84],"pa-rameters":[85],"well":[87],"high":[89],"accuracy":[90,114,131],"capturing":[92],"complicated":[93],"data":[94,105],"in":[95,106],"road":[96],"network.":[97],"Applying":[98],"to":[101,116],"actual":[102],"travel":[103],"trajectory":[104],"Tokyo,":[107],"our":[108],"shows":[111],"higher":[112],"prediction":[113,130],"compared":[115],"Residual":[122],"Network-based":[124],"Recursive":[125],"Moreover,":[128],"interpretability":[134],"can":[135],"be":[136],"balanced":[137],"arbitrarily":[138],"adjusting":[140],"penalty":[142],"coefficient.":[143]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
