{"id":"https://openalex.org/W4398233018","doi":"https://doi.org/10.1145/3652628.3652787","title":"Metro passenger flow prediction: a double-stage decomposition combined with Enhanced-BiGRU model considering multiple factors","display_name":"Metro passenger flow prediction: a double-stage decomposition combined with Enhanced-BiGRU model considering multiple factors","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4398233018","doi":"https://doi.org/10.1145/3652628.3652787"},"language":"en","primary_location":{"id":"doi:10.1145/3652628.3652787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652628.3652787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering","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/A5006981396","display_name":"Zhilei Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I106645853","display_name":"Changchun University of Science and Technology","ror":"https://ror.org/007mntk44","country_code":"CN","type":"education","lineage":["https://openalex.org/I106645853"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhuangzhuang Zhao","raw_affiliation_strings":["Changchun University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Changchun University of Science and Technology, China","institution_ids":["https://openalex.org/I106645853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019858066","display_name":"Di Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I106645853","display_name":"Changchun University of Science and Technology","ror":"https://ror.org/007mntk44","country_code":"CN","type":"education","lineage":["https://openalex.org/I106645853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Yang","raw_affiliation_strings":["Changchun University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Changchun University of Science and Technology, China","institution_ids":["https://openalex.org/I106645853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041440601","display_name":"Peng Wang","orcid":"https://orcid.org/0009-0003-6393-9330"},"institutions":[{"id":"https://openalex.org/I106645853","display_name":"Changchun University of Science and Technology","ror":"https://ror.org/007mntk44","country_code":"CN","type":"education","lineage":["https://openalex.org/I106645853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["Changchun University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Changchun University of Science and Technology, China","institution_ids":["https://openalex.org/I106645853"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036150899","display_name":"Eryan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Eryan Li","raw_affiliation_strings":["Nankai University, China"],"affiliations":[{"raw_affiliation_string":"Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006981396"],"corresponding_institution_ids":["https://openalex.org/I106645853"],"apc_list":null,"apc_paid":null,"fwci":0.1683,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52180749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"957","last_page":"962"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9983000159263611,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9948999881744385,"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/computer-science","display_name":"Computer science","score":0.6125826835632324},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5754985213279724},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.465621680021286},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.42742660641670227},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42326223850250244},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4105774164199829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3005771338939667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17249655723571777}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6125826835632324},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5754985213279724},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.465621680021286},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.42742660641670227},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42326223850250244},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4105774164199829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3005771338939667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17249655723571777},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652628.3652787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652628.3652787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering","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":13,"referenced_works":["https://openalex.org/W2273232783","https://openalex.org/W2969825018","https://openalex.org/W2971250894","https://openalex.org/W3021308819","https://openalex.org/W3108852469","https://openalex.org/W3135788747","https://openalex.org/W3190695173","https://openalex.org/W4220674505","https://openalex.org/W4283764350","https://openalex.org/W4310769220","https://openalex.org/W4311853407","https://openalex.org/W4327967079","https://openalex.org/W4383619258"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W4206951940","https://openalex.org/W4382602594","https://openalex.org/W4387850423"],"abstract_inverted_index":{"Accurate":[0],"and":[1,15,31,55,80,88,137,154,178],"reliable":[2],"passenger":[3,25,47,72,119,125],"flow":[4,26,48,73,126],"prediction":[5,49,127],"is":[6,65,110,159,171],"essential":[7],"to":[8,37,67,112,143,161,183],"formulate":[9],"operational":[10],"plans,":[11],"improve":[12],"service":[13],"quality,":[14],"alleviate":[16],"traffic":[17],"pressure":[18],"for":[19],"the":[20,61,69,76,82,96,114,145,148,163,168,174,184],"metro":[21,46],"transportation":[22],"system.":[23],"However,":[24],"sequences":[27],"exhibit":[28],"strong":[29],"nonlinearity":[30],"random":[32],"fluctuations,":[33],"posing":[34],"significant":[35],"challenges":[36],"prediction.":[38],"To":[39],"address":[40],"this":[41],"problem,":[42],"we":[43,122],"propose":[44],"a":[45,124],"model":[50,170],"based":[51],"on":[52,173],"double-stage":[53,62],"decomposition":[54,63,78,93],"Enhanced-BiGRU":[56],"considering":[57],"multiple":[58],"factors.":[59],"Firstly,":[60],"module":[64,109,128],"constructed":[66],"reduce":[68],"volatility":[70,98],"of":[71,152,165],"sequences.":[74],"Specifically,":[75],"first-stage":[77],"decomposes":[79],"reconstructs":[81],"original":[83],"sequence":[84,97],"into":[85],"high-frequency,":[86],"mid-frequency,":[87],"low-frequency":[89],"components.":[90],"The":[91],"second-stage":[92],"further":[94],"reduces":[95],"by":[99],"decomposing":[100],"complex":[101],"high-frequency":[102],"component.":[103],"Furthermore,":[104],"an":[105],"influence":[106],"factor":[107],"selection":[108],"introduced":[111],"select":[113],"important":[115],"factors":[116],"that":[117],"affect":[118],"flow.":[120],"Finally,":[121],"design":[123],"called":[129],"Enhanced-BiGRU,":[130],"in":[131,147],"which":[132],"convolutional":[133],"neural":[134],"network":[135],"(CNN)":[136],"attention":[138],"mechanism":[139],"(AM)":[140],"are":[141],"employed":[142],"overcome":[144],"deficiency":[146],"feature":[149],"extraction":[150],"capacity":[151],"BiGRU,":[153],"sparrow":[155],"search":[156],"algorithm":[157],"(SSA)":[158],"leveraged":[160],"optimize":[162],"hyperparameters":[164],"BiGRU.":[166],"Experimentally,":[167],"proposed":[169],"validated":[172],"Hangzhou":[175],"Metro":[176],"dataset":[177],"achieves":[179],"superior":[180],"performance":[181],"compared":[182],"state-of-the-art.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
