{"id":"https://openalex.org/W2733322935","doi":"https://doi.org/10.1109/icis.2017.7959977","title":"A forecast for bicycle rental demand based on random forests and multiple linear regression","display_name":"A forecast for bicycle rental demand based on random forests and multiple linear regression","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2733322935","doi":"https://doi.org/10.1109/icis.2017.7959977","mag":"2733322935"},"language":"en","primary_location":{"id":"doi:10.1109/icis.2017.7959977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7959977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","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/A5016997944","display_name":"YouLi Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"YouLi Feng","raw_affiliation_strings":["College of Computer Science and Technology, Inner Mongolia university, Inner Mongolia, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Inner Mongolia university, Inner Mongolia, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100406254","display_name":"Shanshan Wang","orcid":"https://orcid.org/0000-0002-5790-8015"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ShanShan Wang","raw_affiliation_strings":["College of Computer Science and Technology, Inner Mongolia university, Inner Mongolia, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Inner Mongolia university, Inner Mongolia, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016997944"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.5378,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.97244487,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"101","last_page":"105"},"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":0.96670001745224,"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":0.96670001745224,"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":0.9337000250816345,"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/T12546","display_name":"Smart Parking Systems Research","score":0.9143999814987183,"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/renting","display_name":"Renting","score":0.7623741626739502},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6369271278381348},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5941224098205566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.565646231174469},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5435774326324463},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5292937755584717},{"id":"https://openalex.org/keywords/transaction-data","display_name":"Transaction data","score":0.48709774017333984},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.4399407207965851},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3530382215976715},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.25222572684288025},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21554598212242126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18042147159576416},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13325345516204834},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13235455751419067}],"concepts":[{"id":"https://openalex.org/C85502023","wikidata":"https://www.wikidata.org/wiki/Q157171","display_name":"Renting","level":2,"score":0.7623741626739502},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6369271278381348},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5941224098205566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.565646231174469},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5435774326324463},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5292937755584717},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.48709774017333984},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.4399407207965851},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3530382215976715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25222572684288025},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21554598212242126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18042147159576416},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13325345516204834},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13235455751419067},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis.2017.7959977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7959977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7300000190734863,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2070779353","https://openalex.org/W2080520515","https://openalex.org/W2123582781","https://openalex.org/W2392477616","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4313289487","https://openalex.org/W4321636153","https://openalex.org/W2989589039","https://openalex.org/W2780247929","https://openalex.org/W3007554386","https://openalex.org/W3108131348","https://openalex.org/W4213307675"],"abstract_inverted_index":{"Bike":[0,77],"sharing":[1],"system":[2],"is":[3,11,57,114,122],"a":[4,14,20,30,33,147,152],"ways":[5],"of":[6,16,165,174],"renting":[7],"bicycles;":[8],"bike":[9,31,45,71],"return":[10],"automated":[12],"via":[13],"network":[15],"kiosk":[17],"locations":[18],"throughout":[19],"city.":[21],"Using":[22],"these":[23],"systems,":[24],"people":[25],"are":[26,169],"able":[27],"to":[28,46,53,59,69,155,178],"rent":[29],"from":[32],"one":[34],"pick":[35],"up":[36],"location":[37],"and":[38,140,151,162],"combine":[39,60],"with":[40,64,109],"their":[41],"as-need,":[42],"customer":[43],"returns":[44],"the":[47,75,84,92,107,110,117,130,133,138,141,157,163,180],"place,":[48],"which":[49],"they":[50],"would":[51],"prefer":[52],"return.":[54],"This":[55],"paper":[56,145],"asked":[58],"historical":[61],"usage":[62],"patterns":[63],"weather":[65],"data":[66,108,131],"in":[67,74,80],"order":[68],"forecast":[70],"rental":[72],"demand":[73,181],"Capital":[76],"share":[78],"program":[79],"Washington,":[81],"D.C.":[82],"Firstly,":[83],"multiple":[85,118,166],"linear":[86,96,119],"regression":[87,97,120,167],"model":[88,121,150,177],"was":[89,99],"established":[90],"by":[91,101],"conventional":[93],"method,":[94],"Multiple":[95],"equation":[98],"obtained":[100],"using":[102],"SPSS":[103],"software,":[104],"After":[105,125],"comparing":[106],"real":[111],"value,":[112],"it":[113],"indicated":[115],"that":[116,129],"less":[123],"accurate.":[124],"analysis,":[126],"we":[127],"find":[128],"includes":[132],"dummy":[134],"variables":[135],"such":[136],"as":[137],"time":[139],"season.":[142],"Hence":[143],"this":[144],"proposes":[146],"random":[148,175],"forest":[149,176],"GBM":[153],"packet":[154],"improve":[156],"decision":[158],"tree.":[159],"The":[160],"results":[161],"accuracy":[164],"analysis":[168],"greatly":[170],"improved":[171],"when":[172],"use":[173],"predict":[179],"for":[182],"bicycle":[183],"rental.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}