{"id":"https://openalex.org/W4390068922","doi":"https://doi.org/10.1145/3627915.3629593","title":"Inbound Passenger Flow Prediction at Subway Stations Based on lbCNNM-TFT","display_name":"Inbound Passenger Flow Prediction at Subway Stations Based on lbCNNM-TFT","publication_year":2023,"publication_date":"2023-10-17","ids":{"openalex":"https://openalex.org/W4390068922","doi":"https://doi.org/10.1145/3627915.3629593"},"language":"en","primary_location":{"id":"doi:10.1145/3627915.3629593","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627915.3629593","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Computer Science and Application Engineering","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/A5102772286","display_name":"Ziqi Wang","orcid":"https://orcid.org/0009-0001-5722-6495"},"institutions":[{"id":"https://openalex.org/I4210118977","display_name":"Shanghai Tunnel Engineering Rail Transit Design & Research Institute","ror":"https://ror.org/02zznv955","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118977"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Wang","raw_affiliation_strings":["R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0001-5722-6495","affiliations":[{"raw_affiliation_string":"R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China","institution_ids":["https://openalex.org/I4210118977"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088253306","display_name":"Lili Chen","orcid":"https://orcid.org/0000-0002-4623-3454"},"institutions":[{"id":"https://openalex.org/I4210118977","display_name":"Shanghai Tunnel Engineering Rail Transit Design & Research Institute","ror":"https://ror.org/02zznv955","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118977"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Chen","raw_affiliation_strings":["R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China"],"raw_orcid":"https://orcid.org/0000-0002-4623-3454","affiliations":[{"raw_affiliation_string":"R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China","institution_ids":["https://openalex.org/I4210118977"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101790736","display_name":"Wei Mao","orcid":"https://orcid.org/0009-0004-8698-5732"},"institutions":[{"id":"https://openalex.org/I4210118977","display_name":"Shanghai Tunnel Engineering Rail Transit Design & Research Institute","ror":"https://ror.org/02zznv955","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118977"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Mao","raw_affiliation_strings":["R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China"],"raw_orcid":"https://orcid.org/0009-0004-8698-5732","affiliations":[{"raw_affiliation_string":"R&amp;D Department, Nari Rail Transit Technology, Co., Ltd., China","institution_ids":["https://openalex.org/I4210118977"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210118977"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10524","display_name":"Traffic control and management","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10370","display_name":"Traffic and Road Safety","score":0.9739999771118164,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.6591658592224121},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.46462899446487427},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4478946626186371},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4193147122859955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37821775674819946},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.33990657329559326},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32290077209472656},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32221412658691406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.268295019865036},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2512440085411072},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.224604070186615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6591658592224121},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.46462899446487427},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4478946626186371},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4193147122859955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37821775674819946},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.33990657329559326},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32290077209472656},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32221412658691406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.268295019865036},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2512440085411072},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.224604070186615},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627915.3629593","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627915.3629593","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2927719797","https://openalex.org/W2957585919","https://openalex.org/W2971507477","https://openalex.org/W2980994438","https://openalex.org/W2998652672","https://openalex.org/W3084896318","https://openalex.org/W3134251890","https://openalex.org/W3157507404","https://openalex.org/W3171884590","https://openalex.org/W4220674505"],"related_works":["https://openalex.org/W3151522584","https://openalex.org/W1497123311","https://openalex.org/W2090386787","https://openalex.org/W3154635860","https://openalex.org/W4310420093","https://openalex.org/W4226332880","https://openalex.org/W2155226164","https://openalex.org/W2110536527","https://openalex.org/W2364531301","https://openalex.org/W2076634344"],"abstract_inverted_index":{"To":[0],"regulate":[1],"the":[2,59,88,95,112,143,148,169,173],"operation":[3],"of":[4,62,98,111,131,154,193],"metro":[5],"equipment":[6],"based":[7,18,57],"on":[8,19,58],"passenger":[9,14,63,114],"flow":[10,15,64,115],"data":[11,46,81,118,126],"efficiently,":[12],"a":[13,41],"prediction":[16,47,70,92,121],"model":[17,89,140,146,150],"lbCNNM-TFT":[20,39,184],"(Learning-Based":[21],"Convolution":[22],"Nuclear":[23],"Norm":[24],"Minimization":[25],"-":[26],"Temporal":[27],"Fusion":[28],"Transformers)":[29],"for":[30,160,176],"urban":[31],"rail":[32],"transit":[33],"is":[34,40,75],"proposed":[35],"in":[36,191],"this":[37,73],"paper.":[38],"cascaded":[42],"two-stage":[43],"time":[44,79,116],"series":[45,80,117],"network.":[48],"This":[49,100],"network":[50],"uses":[51],"lbCNNM":[52,145],"to":[53,67,107,168],"perform":[54],"matrix":[55],"complementation":[56],"long-term":[60],"features":[61,110],"sequences":[65],"firstly":[66],"obtain":[68,108,120],"rough":[69],"results.":[71],"Then,":[72],"result":[74],"fused":[76],"with":[77],"short-term":[78],"and":[82,104,119,165,181],"some":[83],"external":[84],"relevant":[85],"features.":[86],"Finally,":[87],"outputs":[90],"refined":[91],"values":[93,159,175],"through":[94],"encoding-decoding":[96],"struct":[97],"TFT.":[99],"method":[101],"combines":[102],"non-learning":[103],"learning":[105,189],"methods":[106],"multi-model":[109],"subway":[113,134],"results":[122],"comprehensively.":[123],"The":[124,157],"experimental":[125],"obtained":[127],"from":[128],"15":[129,155],"stations":[130],"an":[132],"actual":[133],"line":[135],"shows":[136],"that":[137],"our":[138],"combined":[139],"outperforms":[141,185],"both":[142],"single":[144],"or":[147],"TFT":[149],"at":[151],"13":[152],"out":[153],"stations.":[156],"mean":[158,174],"MAE":[161],"decrease":[162,178],"by":[163,179],"34.2":[164],"6.8":[166],"compared":[167],"two":[170],"models,":[171],"while":[172],"RMSE":[177],"45.9":[180],"8.2.":[182],"Additionally,":[183],"other":[186],"current":[187],"deep":[188],"models":[190],"terms":[192],"accuracy.":[194]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
