{"id":"https://openalex.org/W2775209941","doi":"https://doi.org/10.1109/smc.2017.8122730","title":"Predicting the number of driving service orders in fine-grained regions by an ensemble multi-view-based model","display_name":"Predicting the number of driving service orders in fine-grained regions by an ensemble multi-view-based model","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2775209941","doi":"https://doi.org/10.1109/smc.2017.8122730","mag":"2775209941"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5100372695","display_name":"Pei Luo","orcid":"https://orcid.org/0000-0003-3095-9223"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pei Luo","raw_affiliation_strings":["School of Comp. & Tech., Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Comp. & Tech., Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393243","display_name":"Xin Xin","orcid":"https://orcid.org/0000-0002-0965-5930"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xin","raw_affiliation_strings":["School of Comp. & Tech., Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Comp. & Tech., Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034904926","display_name":"Kuai Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuai Zhang","raw_affiliation_strings":["School of Comp. & Tech., Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Comp. & Tech., Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020083340","display_name":"Ping Guo","orcid":"https://orcid.org/0000-0002-7122-1084"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Guo","raw_affiliation_strings":["School of Systems Science, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Systems Science, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101518165","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0002-0305-478X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["Beijing YiXin YiXing Auto Technology, Development Service Company Limited, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing YiXin YiXing Auto Technology, Development Service Company Limited, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018162534","display_name":"Zichao Wang","orcid":"https://orcid.org/0000-0001-5375-2669"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zichao Wang","raw_affiliation_strings":["Beijing YiXin YiXing Auto Technology, Development Service Company Limited, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing YiXin YiXing Auto Technology, Development Service Company Limited, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23941184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"68","issue":null,"first_page":"936","last_page":"941"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9987999796867371,"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/T12306","display_name":"Urban and Freight Transport Logistics","score":0.9811999797821045,"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":0.7462567687034607},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.6831135749816895},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6614425182342529},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6336703896522522},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5753035545349121},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.5063349008560181},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49668580293655396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4731115996837616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4249257743358612},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.4217078685760498},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15167468786239624},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12973615527153015}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462567687034607},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.6831135749816895},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6614425182342529},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6336703896522522},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5753035545349121},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.5063349008560181},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49668580293655396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4731115996837616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4249257743358612},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.4217078685760498},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15167468786239624},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12973615527153015},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335569","display_name":"Joint Fund of Astronomy","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1636244751","https://openalex.org/W2082418604","https://openalex.org/W2097056253","https://openalex.org/W2110485445","https://openalex.org/W2128302979","https://openalex.org/W2129018774","https://openalex.org/W2143039774","https://openalex.org/W2147880316","https://openalex.org/W2150964976","https://openalex.org/W2158382689","https://openalex.org/W2233560226","https://openalex.org/W2260671954","https://openalex.org/W2313953460","https://openalex.org/W2516051559","https://openalex.org/W2528639018","https://openalex.org/W2592817493","https://openalex.org/W2798056406","https://openalex.org/W2902455138","https://openalex.org/W2952740813","https://openalex.org/W4254816979","https://openalex.org/W4292081177","https://openalex.org/W4292483811","https://openalex.org/W6677096361","https://openalex.org/W6682408398","https://openalex.org/W6683424812","https://openalex.org/W6692508403"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2369897927","https://openalex.org/W3031731056","https://openalex.org/W4293167957","https://openalex.org/W2361035307","https://openalex.org/W2380455807","https://openalex.org/W2993975634","https://openalex.org/W2367835030"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"driving":[2],"service":[3,13,20],"orders":[4,97],"in":[5,15,98,115,130],"different":[6,40],"regions":[7,92],"is":[8,31,72,87],"an":[9],"essential":[10],"task":[11],"for":[12],"companies,":[14],"order":[16,113],"to":[17,33,74,89,93],"improve":[18],"the":[19,76,119],"quality.":[21],"In":[22,64,81],"this":[23,35],"paper,":[24],"a":[25,44,67,84],"specific":[26,53,85],"ensemble":[27],"multi-view":[28],"prediction":[29,69],"framework":[30,121],"proposed":[32,120],"address":[34],"task.":[36],"It":[37],"ensembles":[38],"several":[39],"multi-view-based":[41,54,127],"models":[42,128],"with":[43],"weighted":[45],"linear":[46],"combination.":[47],"Specifically,":[48],"we":[49],"have":[50],"designed":[51,88],"three":[52],"models,":[55],"which":[56],"of":[57,62,132],"them":[58],"contains":[59],"two":[60,102],"types":[61],"views.":[63],"first":[65],"view,":[66,83],"spatio-temporal":[68],"model":[70],"(ST-Model)":[71],"employed":[73],"construct":[75],"features":[77],"on":[78,111],"historical":[79],"orders.":[80],"second":[82],"CRF":[86],"joint":[90],"adjacent":[91],"collaborate":[94],"predict":[95],"future":[96],"these":[99],"regions.":[100],"The":[101],"views":[103],"are":[104],"learned":[105],"and":[106,125,137],"inferred":[107],"simultaneously.":[108],"Extensive":[109],"evaluations":[110],"real":[112],"data":[114],"Beijing":[116],"show":[117],"that":[118],"outperforms":[122],"all":[123],"baselines":[124],"participated":[126],"significantly":[129],"terms":[131],"mean":[133,139],"absolute":[134],"error":[135,141],"(MAE)":[136],"root":[138],"square":[140],"(RMSE).":[142]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}