{"id":"https://openalex.org/W4399562951","doi":"https://doi.org/10.1109/tcss.2024.3402735","title":"Novel Confined Attention-Enabled Hierarchical Bi-LSTM and Bi-GRU Fusion: A Multiscale Traffic Flow Prediction Application","display_name":"Novel Confined Attention-Enabled Hierarchical Bi-LSTM and Bi-GRU Fusion: A Multiscale Traffic Flow Prediction Application","publication_year":2024,"publication_date":"2024-06-12","ids":{"openalex":"https://openalex.org/W4399562951","doi":"https://doi.org/10.1109/tcss.2024.3402735"},"language":"en","primary_location":{"id":"doi:10.1109/tcss.2024.3402735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2024.3402735","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"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 Transactions on Computational Social Systems","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/A5002019375","display_name":"Nisha Singh Chauhan","orcid":"https://orcid.org/0000-0003-1846-5458"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nisha Singh Chauhan","raw_affiliation_strings":["Indian Institute of Technology, Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0003-1846-5458","affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002763824","display_name":"Neetesh Kumar","orcid":"https://orcid.org/0000-0002-0516-1095"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neetesh Kumar","raw_affiliation_strings":["Indian Institute of Technology, Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0002-0516-1095","affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":2.279,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.86746963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":"6","first_page":"7541","last_page":"7554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"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":0.9991999864578247,"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/T10320","display_name":"Neural Networks and Applications","score":0.9682000279426575,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9067000150680542,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision 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":0.5373526811599731},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5130289793014526},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.49187755584716797},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3597831726074219}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5373526811599731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5130289793014526},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.49187755584716797},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3597831726074219},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcss.2024.3402735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2024.3402735","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"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 Transactions on Computational Social Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2073640212","https://openalex.org/W2115032462","https://openalex.org/W2171234954","https://openalex.org/W2573587735","https://openalex.org/W2754252319","https://openalex.org/W2782391880","https://openalex.org/W2808097153","https://openalex.org/W2891280833","https://openalex.org/W2903871660","https://openalex.org/W2916664939","https://openalex.org/W2926209979","https://openalex.org/W2960749694","https://openalex.org/W2973546771","https://openalex.org/W2974087501","https://openalex.org/W2991205212","https://openalex.org/W2994901268","https://openalex.org/W2997848713","https://openalex.org/W3001329848","https://openalex.org/W3003622852","https://openalex.org/W3007926275","https://openalex.org/W3027664001","https://openalex.org/W3038719270","https://openalex.org/W3046773429","https://openalex.org/W3080253043","https://openalex.org/W3089040363","https://openalex.org/W3112830724","https://openalex.org/W3134587999","https://openalex.org/W3135400423","https://openalex.org/W3160663839","https://openalex.org/W3164487383","https://openalex.org/W3168048627","https://openalex.org/W3185475837","https://openalex.org/W3193512724","https://openalex.org/W4280581140","https://openalex.org/W4297380734","https://openalex.org/W4322755838","https://openalex.org/W4383749586","https://openalex.org/W4393032924","https://openalex.org/W6631190155","https://openalex.org/W6679434410","https://openalex.org/W6746015598","https://openalex.org/W6780186301"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,72,120,151],"prediction":[2],"is":[3,130,154,182],"essential":[4],"for":[5,36,205],"alleviating":[6],"congestion":[7],"and":[8,25,40,63,68,93,99,104,192,195],"enhancing":[9],"traffic":[10,71,119,150],"management.":[11],"While":[12],"previous":[13],"efforts":[14],"have":[15,32],"mainly":[16],"concentrated":[17],"on":[18,121,143,184],"road-level":[19],"predictions,":[20],"the":[21,26,57,61,75,102,108,118,122,133,138,149,157,164,169,176,196,206],"advancement":[22],"of":[23,29],"technology":[24],"growing":[27],"presence":[28],"automated":[30],"vehicles":[31],"encouraged":[33],"lane-level":[34,58,76],"forecasting":[35],"more":[37,145],"efficient":[38],"driving":[39],"routing":[41],"decisions.":[42],"Therefore,":[43],"this":[44],"work":[45],"presents":[46],"a":[47],"hierarchical":[48],"model":[49,181],"using":[50,80,87],"recurrent":[51,96],"neural":[52],"networks":[53],"(RNNs)":[54],"that":[55,156],"takes":[56],"information":[59,147],"as":[60],"input":[62],"makes":[64],"multiscale":[65],"(lane":[66],"level":[67],"road":[69],"level)":[70],"predictions.":[73],"Initially,":[74],"predictions":[77],"are":[78,114,172,203],"made":[79],"confined":[81,127,158],"attention":[82,128,159,166],"enabled":[83],"RNN":[84],"(CAERNN),":[85],"built":[86],"bidirection":[88,94],"long":[89],"short-term":[90],"memory":[91],"(Bi-LSTM)":[92],"gated":[95],"unit":[97],"(Bi-GRU),":[98],"separately":[100],"learning":[101],"periodic":[103],"temporal":[105],"characteristics.":[106],"Further,":[107],"features":[109,171],"extracted":[110],"by":[111,141],"each":[112],"lane":[113],"used":[115],"to":[116,136,174],"predict":[117],"entire":[123],"road.":[124],"A":[125],"novel":[126],"mechanism":[129,160],"integrated":[131,173],"into":[132],"Bi-LSTM":[134],"module":[135],"improve":[137,175],"model's":[139,177],"performance":[140],"focusing":[142],"recent,":[144],"relevant":[146],"in":[148],"sequence.":[152],"It":[153],"observed":[155],"performs":[161],"better":[162],"than":[163],"conventional":[165],"mechanism.":[167],"Furthermore,":[168],"external":[170],"performance.":[178],"The":[179],"proposed":[180],"evaluated":[183],"publicly":[185],"available":[186],"real-world":[187],"data":[188],"sets":[189],"(in":[190],"normal":[191],"COVID-19":[193],"scenarios),":[194],"compared":[197],"results":[198],"with":[199],"several":[200],"state-of-the-art":[201],"methods":[202],"evidence":[204],"CAERNN's":[207],"effectiveness.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}