{"id":"https://openalex.org/W4366306858","doi":"https://doi.org/10.1109/icite56321.2022.10101458","title":"Ensemble Empirical Mode Decomposition-Based Gated Recurrent Unit Model for Short-Term Metro Passenger Flow Prediction","display_name":"Ensemble Empirical Mode Decomposition-Based Gated Recurrent Unit Model for Short-Term Metro Passenger Flow Prediction","publication_year":2022,"publication_date":"2022-11-11","ids":{"openalex":"https://openalex.org/W4366306858","doi":"https://doi.org/10.1109/icite56321.2022.10101458"},"language":"en","primary_location":{"id":"doi:10.1109/icite56321.2022.10101458","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite56321.2022.10101458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","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/A5081384659","display_name":"Zhanru Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhanru Liu","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012050038","display_name":"Cong Xiu","orcid":"https://orcid.org/0000-0002-6393-7883"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Xiu","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024225425","display_name":"Yichen Sun","orcid":"https://orcid.org/0000-0002-7399-6107"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yichen Sun","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101719116","display_name":"Bin Shuai","orcid":"https://orcid.org/0000-0002-9860-9396"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Shuai","raw_affiliation_strings":["Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Transportation and Logistics,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081384659"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":0.2117,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51646979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"248"},"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":0.9977999925613403,"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":0.9947999715805054,"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/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.8024270534515381},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6291546821594238},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5665783286094666},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5661841630935669},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.539191484451294},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5350827574729919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5250111222267151},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.45536062121391296},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4360199570655823},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.34164923429489136},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2262539565563202},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12384495139122009}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.8024270534515381},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6291546821594238},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5665783286094666},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5661841630935669},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.539191484451294},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5350827574729919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5250111222267151},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.45536062121391296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4360199570655823},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34164923429489136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2262539565563202},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12384495139122009},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icite56321.2022.10101458","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite56321.2022.10101458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7699999809265137,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W2001549036","https://openalex.org/W2002841906","https://openalex.org/W2075795319","https://openalex.org/W2085987121","https://openalex.org/W2088353498","https://openalex.org/W2117130368","https://openalex.org/W2120390927","https://openalex.org/W2150010190","https://openalex.org/W2270082749","https://openalex.org/W2564701384","https://openalex.org/W2580845658","https://openalex.org/W2615673769","https://openalex.org/W2783913168","https://openalex.org/W2790796102","https://openalex.org/W2901295635","https://openalex.org/W2927719797","https://openalex.org/W2946964936","https://openalex.org/W2957585919","https://openalex.org/W2970228697","https://openalex.org/W2972449950","https://openalex.org/W2973672392","https://openalex.org/W3000764024","https://openalex.org/W4205947740","https://openalex.org/W4220674505","https://openalex.org/W6640212811"],"related_works":["https://openalex.org/W3014107421","https://openalex.org/W2081563414","https://openalex.org/W2363056446","https://openalex.org/W2359718298","https://openalex.org/W2377062149","https://openalex.org/W2076661204","https://openalex.org/W2380939102","https://openalex.org/W2089603224","https://openalex.org/W2329112433","https://openalex.org/W1991001811"],"abstract_inverted_index":{"An":[0],"accurate":[1],"short-term":[2,18,67,208],"passenger":[3,20,68,81,118,126,156,210],"flow":[4,21,69,82,127,157,211],"prediction":[5,34],"provides":[6],"essential":[7],"data":[8,22,83,146],"support":[9],"for":[10,66,132,206],"Urban":[11],"Rail":[12],"Transit":[13],"(URT)":[14],"organizations.":[15],"However,":[16],"the":[17,31,77,95,105,116,121,133,138,151,155,169,176,190,197],"URT":[19,117],"is":[23],"neither":[24],"linear":[25],"nor":[26],"stationary,":[27],"which":[28,202],"results":[29,65,173],"in":[30],"inaccuracy":[32],"of":[33,140,158,162],"using":[35],"only":[36],"single":[37],"deep-learning":[38],"models.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43],"proposed":[44,177],"a":[45,91,160],"hybrid":[46],"model":[47],"that":[48,111,175],"integrates":[49],"Ensemble":[50],"Empirical":[51],"Mode":[52],"Decomposition":[53],"(EEMD)":[54],"and":[55,63,90,97,124,144,195],"Gated":[56],"Recurrent":[57],"Unit":[58],"(GRU)":[59],"to":[60,103,108,115,149,167],"achieve":[61],"faster":[62],"better":[64],"prediction.":[70],"The":[71,171],"approach":[72],"contains":[73],"four":[74],"steps.":[75],"First,":[76],"EEMD":[78],"algorithm":[79],"decomposes":[80],"into":[84],"several":[85],"intrinsic":[86],"mode":[87],"functions":[88],"(IMF)":[89],"residual":[92],"function.":[93],"Second,":[94],"Spearman":[96],"Kendall":[98],"correlation":[99],"coefficients":[100],"were":[101,147],"used":[102,154],"examine":[104],"IMF":[106],"components":[107,110],"select":[109],"are":[112,128],"highly":[113],"related":[114],"flow.":[119],"Third,":[120],"selected":[122],"features":[123],"historical":[125,145],"combined":[129],"as":[130],"inputs":[131],"GRU":[134],"model,":[135],"respectively.":[136],"Last,":[137],"outputs":[139],"each":[141],"input":[142],"feature":[143],"synthesized":[148],"obtain":[150],"result.":[152],"We":[153],"Xipu,":[159],"station":[161],"Chengdu":[163],"Metro":[164],"Line":[165],"2,":[166],"demonstrate":[168],"method.":[170],"experimental":[172],"indicate":[174],"EEMD-GRU":[178],"architecture,":[179],"compared":[180],"with":[181],"benchmark":[182],"models,":[183],"achieves":[184],"superior":[185],"performance.":[186],"It":[187],"can":[188],"reduce":[189],"training":[191],"time":[192],"by":[193,200],"26.18%":[194],"improve":[196],"average":[198],"accuracy":[199],"29.58%,":[201],"shows":[203],"great":[204],"validity":[205],"large-scale":[207],"metro":[209],"forecasting.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}