{"id":"https://openalex.org/W3197083997","doi":"https://doi.org/10.1109/mt-its49943.2021.9529275","title":"Origin-destination matrix estimation by deep learning using maps with New York case study","display_name":"Origin-destination matrix estimation by deep learning using maps with New York case study","publication_year":2021,"publication_date":"2021-06-16","ids":{"openalex":"https://openalex.org/W3197083997","doi":"https://doi.org/10.1109/mt-its49943.2021.9529275","mag":"3197083997"},"language":"en","primary_location":{"id":"doi:10.1109/mt-its49943.2021.9529275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its49943.2021.9529275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5080887412","display_name":"Danyel Koca","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Danyel Koca","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027022814","display_name":"Jan Dirk Schmocker","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jan Dirk Schmocker","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108268907","display_name":"Kouji Fukuda","orcid":null},"institutions":[{"id":"https://openalex.org/I65143321","display_name":"Hitachi (Japan)","ror":"https://ror.org/02exqgm79","country_code":"JP","type":"company","lineage":["https://openalex.org/I65143321"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kouji Fukuda","raw_affiliation_strings":["Research and Development Group, Global Center for Social innovation, Hitachi Ltd., Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Group, Global Center for Social innovation, Hitachi Ltd., Tokyo, Japan","institution_ids":["https://openalex.org/I65143321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0924,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77690892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9940999746322632,"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":0.9936000108718872,"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/computer-science","display_name":"Computer science","score":0.7118238806724548},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.7021546363830566},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.5642136335372925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5548563003540039},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5395400524139404},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5141863822937012},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4689927399158478},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4553731679916382},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42437565326690674},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42029285430908203},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41306623816490173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40595483779907227},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15058371424674988},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1330801248550415},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1255703866481781}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7118238806724548},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.7021546363830566},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.5642136335372925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5548563003540039},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5395400524139404},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5141863822937012},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4689927399158478},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4553731679916382},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42437565326690674},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42029285430908203},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41306623816490173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40595483779907227},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15058371424674988},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1330801248550415},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1255703866481781},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"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/mt-its49943.2021.9529275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its49943.2021.9529275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W32556693","https://openalex.org/W308787633","https://openalex.org/W587009042","https://openalex.org/W1522301498","https://openalex.org/W1550425799","https://openalex.org/W1967840600","https://openalex.org/W1979081979","https://openalex.org/W1992228222","https://openalex.org/W2020281294","https://openalex.org/W2056520705","https://openalex.org/W2294927176","https://openalex.org/W2519887557","https://openalex.org/W2520173338","https://openalex.org/W2555251031","https://openalex.org/W2799787002","https://openalex.org/W2890072063","https://openalex.org/W2905224888","https://openalex.org/W2911567805","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W3009365266","https://openalex.org/W3017680305","https://openalex.org/W3057149475","https://openalex.org/W3094715102","https://openalex.org/W6601307240","https://openalex.org/W6617257849","https://openalex.org/W6631190155","https://openalex.org/W6726873649","https://openalex.org/W6727057654","https://openalex.org/W6757374366"],"related_works":["https://openalex.org/W2807758032","https://openalex.org/W4224254130","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W848352814"],"abstract_inverted_index":{"Origin-destination":[0],"(OD)":[1],"matrix":[2,100],"estimation":[3],"has":[4],"conventionally":[5],"required":[6],"costly":[7],"surveys":[8],"or":[9,25],"other":[10],"forms":[11],"of":[12,42],"sensor":[13],"data.":[14],"In":[15],"this":[16],"paper,":[17],"we":[18],"propose":[19],"that":[20],"map":[21],"data":[22,29,45],"can":[23],"supplement":[24],"partially":[26],"replace":[27],"such":[28],"if":[30],"integrated":[31],"in":[32,86],"a":[33,53,60,64],"deep":[34],"learning":[35,57],"method.":[36],"Road":[37],"network":[38],"information":[39],"and":[40,88,92],"point":[41],"interest":[43],"(POI)":[44],"are":[46],"used":[47],"for":[48,82,96],"predicting":[49],"interzonal":[50],"trips":[51,85],"within":[52],"city.":[54],"The":[55],"proposed":[56],"approach":[58,67],"is":[59],"hybrid":[61],"model,":[62],"combining":[63],"graph":[65],"convolutional":[66],"at":[68],"the":[69],"lower":[70],"level":[71,75],"with":[72,101],"an":[73,98],"upper":[74],"multilayer":[76],"perceptron.":[77],"We":[78],"apply":[79],"our":[80],"method":[81],"estimating":[83],"taxi":[84],"Manhattan":[87],"Brooklyn,":[89],"New":[90],"York,":[91],"obtain":[93],"promising":[94],"results":[95],"expanding":[97],"OD":[99],"limited":[102],"observations.":[103]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}