{"id":"https://openalex.org/W4226397656","doi":"https://doi.org/10.1109/mits.2022.3158631","title":"A Fusion Deep Learning Model via Sequence-to-Sequence Structure for Multiple-Road-Segment Spot Speed Prediction","display_name":"A Fusion Deep Learning Model via Sequence-to-Sequence Structure for Multiple-Road-Segment Spot Speed Prediction","publication_year":2022,"publication_date":"2022-03-30","ids":{"openalex":"https://openalex.org/W4226397656","doi":"https://doi.org/10.1109/mits.2022.3158631"},"language":"en","primary_location":{"id":"doi:10.1109/mits.2022.3158631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mits.2022.3158631","pdf_url":null,"source":{"id":"https://openalex.org/S131000621","display_name":"IEEE Intelligent Transportation Systems Magazine","issn_l":"1939-1390","issn":["1939-1390","1941-1197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Intelligent Transportation Systems Magazine","raw_type":"journal-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/A5014130344","display_name":"Dongyi Li","orcid":"https://orcid.org/0000-0001-7637-6344"},"institutions":[{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongyi Li","raw_affiliation_strings":["Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang’an University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang’an University, Xi’an, China","institution_ids":["https://openalex.org/I4210127216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064915177","display_name":"Jianjun Wang","orcid":"https://orcid.org/0000-0002-6548-2894"},"institutions":[{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianjun Wang","raw_affiliation_strings":["Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang’an University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang’an University, Xi’an, China","institution_ids":["https://openalex.org/I4210127216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014130344"],"corresponding_institution_ids":["https://openalex.org/I4210127216"],"apc_list":null,"apc_paid":null,"fwci":0.2117,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.47429456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"15","issue":"1","first_page":"230","last_page":"243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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":0.9923999905586243,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9902999997138977,"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/sequence","display_name":"Sequence (biology)","score":0.6720000505447388},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6574024558067322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6327423453330994},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5682672262191772},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5669747591018677},{"id":"https://openalex.org/keywords/fusion-mechanism","display_name":"Fusion mechanism","score":0.5326331257820129},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5093404054641724},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4771254062652588},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4581124186515808},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.41848224401474},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.37907350063323975},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32987284660339355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11020547151565552}],"concepts":[{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6720000505447388},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6574024558067322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6327423453330994},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5682672262191772},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5669747591018677},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.5326331257820129},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5093404054641724},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4771254062652588},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4581124186515808},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.41848224401474},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.37907350063323975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32987284660339355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11020547151565552},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C103038307","wikidata":"https://www.wikidata.org/wiki/Q6556360","display_name":"Lipid bilayer fusion","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mits.2022.3158631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mits.2022.3158631","pdf_url":null,"source":{"id":"https://openalex.org/S131000621","display_name":"IEEE Intelligent Transportation Systems Magazine","issn_l":"1939-1390","issn":["1939-1390","1941-1197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Intelligent Transportation Systems Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2004353783","https://openalex.org/W2036785686","https://openalex.org/W2062017159","https://openalex.org/W2075824254","https://openalex.org/W2119460626","https://openalex.org/W2135729142","https://openalex.org/W2150010190","https://openalex.org/W2157331557","https://openalex.org/W2467914952","https://openalex.org/W2528639018","https://openalex.org/W2579495707","https://openalex.org/W2613331518","https://openalex.org/W2775717462","https://openalex.org/W2793820729","https://openalex.org/W2883216673","https://openalex.org/W2895340898","https://openalex.org/W2900471328","https://openalex.org/W2922426219","https://openalex.org/W2953729237","https://openalex.org/W2956159833","https://openalex.org/W2962790412","https://openalex.org/W2963358464","https://openalex.org/W2976168788","https://openalex.org/W3000499162","https://openalex.org/W3012295089","https://openalex.org/W3035338169","https://openalex.org/W3113160599","https://openalex.org/W3157911332","https://openalex.org/W6628877408","https://openalex.org/W6631190155","https://openalex.org/W6679436768","https://openalex.org/W6746015598"],"related_works":["https://openalex.org/W4321369474","https://openalex.org/W3156786002","https://openalex.org/W4381487685","https://openalex.org/W2738221750","https://openalex.org/W3021430260","https://openalex.org/W564581980","https://openalex.org/W2337926734","https://openalex.org/W3136076031","https://openalex.org/W4366224123","https://openalex.org/W4320802194"],"abstract_inverted_index":{"Forecasting":[0],"the":[1,11,26,51,70,98,108,112,118,132,135,159,166,170,195,202],"future":[2],"average":[3],"speed":[4,28,48,144,207],"fluctuation":[5],"is":[6,60,63,115,137],"conducive":[7],"to":[8,87,156,165,178],"timely":[9],"perceiving":[10],"degree":[12],"of":[13,111,134,142,149,169,205],"urban":[14,31],"road":[15,32,38,147,210],"congestion.":[16],"Nevertheless,":[17],"a":[18,64,146,179,186,192],"new":[19],"challenge":[20],"has":[21],"arisen":[22],"in":[23,145,201],"accurately":[24],"predicting":[25],"spot":[27,47,143,206],"on":[29,128],"an":[30,43,140],"network":[33,148,184,188],"area":[34],"composed":[35],"by":[36,103,117],"multiple":[37,209],"segments.":[39,211],"To":[40],"surmount":[41],"this,":[42],"intelligent":[44],"method":[45],"for":[46,208],"estimation,":[49],"namely,":[50],"fusion":[52],"deep":[53],"learning":[54,66],"model":[55,114,126,136,197],"with":[56,139,189],"sequence-to-sequence":[57,65],"structure":[58,67],"(Seq2Seq-FDL),":[59],"proposed.":[61],"It":[62],"that":[68,158],"covers":[69],"encoding":[71],"context":[72],"vector.":[73],"The":[74,151,173],"unidirectional":[75],"convolutional":[76,180],"long":[77],"short-term":[78,90],"memory":[79],"(ConvLSTM)":[80],"and":[81,91,122,131,185],"bidirectional":[82,193],"ConvLSTM":[83,187],"networks":[84],"are":[85,101],"used":[86],"capture":[88],"traffic":[89,160],"periodic":[92],"spatiotemporal":[93,161],"synchronization":[94],"characteristics,":[95],"respectively.":[96],"Finally,":[97],"prediction":[99,167,174,204],"results":[100,153],"computed":[102],"linear":[104],"feature":[105],"fusion.":[106],"Furthermore,":[107],"black-box":[109],"effect":[110,168],"proposed":[113,196],"explained":[116],"least":[119],"absolute":[120],"shrinkage":[121],"selection":[123],"operator":[124],"regression":[125],"based":[127],"Granger":[129],"causality,":[130],"rationality":[133],"verified":[138],"example":[141],"Guangzhou.":[150],"experimental":[152],"were":[154],"shown":[155],"indicate":[157],"characteristics":[162],"significantly":[163],"contributed":[164],"Seq2Seq-FDL":[171],"model.":[172],"accuracy":[175],"was":[176],"superior":[177],"neural":[181],"network-connected":[182],"LSTM":[183],"or":[190],"without":[191],"LSTM;":[194],"presented":[198],"good":[199],"applicability":[200],"multiple-time-step":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}