{"id":"https://openalex.org/W2981543173","doi":"https://doi.org/10.1145/3356995.3364543","title":"Bike-Share Demand Prediction using Attention based Sequence to Sequence and Conditional Variational AutoEncoder","display_name":"Bike-Share Demand Prediction using Attention based Sequence to Sequence and Conditional Variational AutoEncoder","publication_year":2019,"publication_date":"2019-10-23","ids":{"openalex":"https://openalex.org/W2981543173","doi":"https://doi.org/10.1145/3356995.3364543","mag":"2981543173"},"language":"en","primary_location":{"id":"doi:10.1145/3356995.3364543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356995.3364543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility","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/A5026707664","display_name":"Tomohiro Mimura","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tomohiro Mimura","raw_affiliation_strings":["NTT DOCOMO, INC., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039745517","display_name":"Shin Ishiguro","orcid":"https://orcid.org/0009-0005-8097-8360"},"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":"Shin Ishiguro","raw_affiliation_strings":["NTT DOCOMO, INC., The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102268165","display_name":"Satoshi Kawasaki","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":false,"raw_author_name":"Satoshi Kawasaki","raw_affiliation_strings":["NTT DOCOMO, INC., The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029670252","display_name":"Yusuke Fukazawa","orcid":"https://orcid.org/0000-0001-9834-9339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusuke Fukazawa","raw_affiliation_strings":["NTT DOCOMO, INC., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026707664"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.747,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.8947058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"41","last_page":"44"},"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":0.9854000210762024,"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":0.9854000210762024,"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":0.9686999917030334,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9383999705314636,"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/autoencoder","display_name":"Autoencoder","score":0.7795429825782776},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.7201273441314697},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6263020634651184},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5503357648849487},{"id":"https://openalex.org/keywords/port","display_name":"Port (circuit theory)","score":0.45644667744636536},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4416447877883911},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4179304242134094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34788191318511963},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2893579602241516},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28044986724853516},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21882227063179016},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21844476461410522},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1590147614479065}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7795429825782776},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.7201273441314697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6263020634651184},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5503357648849487},{"id":"https://openalex.org/C32802771","wikidata":"https://www.wikidata.org/wiki/Q2443617","display_name":"Port (circuit theory)","level":2,"score":0.45644667744636536},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4416447877883911},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4179304242134094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34788191318511963},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2893579602241516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28044986724853516},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21882227063179016},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21844476461410522},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1590147614479065},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3356995.3364543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356995.3364543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1754346988","https://openalex.org/W1959608418","https://openalex.org/W2770426760","https://openalex.org/W2795252437","https://openalex.org/W2809079004","https://openalex.org/W2809303669","https://openalex.org/W2809664688","https://openalex.org/W2898879857","https://openalex.org/W4311415873"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"In":[0,65,133],"recent":[1],"years,":[2],"bicycle":[3],"sharing":[4],"services":[5],"(bike-shares)":[6],"have":[7],"been":[8],"established":[9],"worldwide.":[10],"One":[11],"important":[12],"aspect":[13],"of":[14,23,38,60,79],"bike-share":[15,51],"management":[16],"is":[17,54],"to":[18,44,56,72,91,117,150],"periodically":[19],"rebalance":[20,49],"the":[21,24,28,36,58,77,108,113,134],"positions":[22],"available":[25],"bikes.":[26],"Because":[27],"bike":[29,74,80],"demand":[30,75],"varies":[31],"by":[32],"and":[33,76,82,112],"over":[34],"time,":[35],"number":[37,59,78],"bikes":[39,61],"at":[40,85],"each":[41,63,86],"bike-port":[42,87],"tends":[43],"become":[45],"unbalanced.":[46],"To":[47,96],"efficiently":[48],"a":[50,70,101],"system,":[52],"it":[53],"essential":[55],"predicting":[57,73],"in":[62,94,143],"bike-port.":[64],"this":[66,123],"paper,":[67],"we":[68,99],"propose":[69],"method":[71,124,138],"pickups":[81],"drop":[83],"offs":[84],"every":[88],"hour,":[89],"up":[90],"24":[92],"hours":[93],"advance.":[95],"predict":[97],"demand,":[98],"used":[100],"time":[102],"series":[103],"generation":[104],"model":[105,111],"based":[106,115],"on":[107],"Variational":[109,126],"Autoencoders":[110,127],"Attention":[114],"Sequence":[116,118],"learning":[119],"model.":[120],"We":[121],"named":[122],"\"Conditional":[125],"considering":[128],"Partial":[129],"Future":[130],"data\"":[131],"(CVAE-PF).":[132],"experiment,":[135],"our":[136],"proposed":[137],"showed":[139],"higher":[140],"prediction":[141],"accuracy":[142],"root":[144],"mean":[145],"square":[146],"error":[147],"(RMSE)":[148],"compared":[149],"conventional":[151],"methods.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}