{"id":"https://openalex.org/W3197184836","doi":"https://doi.org/10.1080/15472450.2021.1974857","title":"Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm","display_name":"Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm","publication_year":2021,"publication_date":"2021-09-06","ids":{"openalex":"https://openalex.org/W3197184836","doi":"https://doi.org/10.1080/15472450.2021.1974857","mag":"3197184836"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2021.1974857","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2021.1974857","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://orbi.umons.ac.be/bitstream/20.500.12907/42415/1/Metaheuristic_enabled_deep_convolutional_neural_network_for_traffic_flow_prediction_Impact_of_improved_lion_algorithm.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102853310","display_name":"M. B. Patel","orcid":"https://orcid.org/0000-0002-8465-6511"},"institutions":[{"id":"https://openalex.org/I9694494","display_name":"Parul University","ror":"https://ror.org/024v3fg07","country_code":"IN","type":"education","lineage":["https://openalex.org/I9694494"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Monal Patel","raw_affiliation_strings":["Parul University, Vadodara, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Parul University, Vadodara, Gujarat, India","institution_ids":["https://openalex.org/I9694494"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053407644","display_name":"Carlos Valderrama","orcid":"https://orcid.org/0000-0002-1693-6394"},"institutions":[{"id":"https://openalex.org/I130929987","display_name":"University of Mons","ror":"https://ror.org/02qnnz951","country_code":"BE","type":"education","lineage":["https://openalex.org/I130929987"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Carlos Valderrama","raw_affiliation_strings":["University of Mons, Mons, Belgium"],"affiliations":[{"raw_affiliation_string":"University of Mons, Mons, Belgium","institution_ids":["https://openalex.org/I130929987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003935044","display_name":"Arvind Yadav","orcid":"https://orcid.org/0000-0002-8988-9947"},"institutions":[{"id":"https://openalex.org/I9694494","display_name":"Parul University","ror":"https://ror.org/024v3fg07","country_code":"IN","type":"education","lineage":["https://openalex.org/I9694494"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arvind Yadav","raw_affiliation_strings":["Parul University, Vadodara, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Parul University, Vadodara, Gujarat, India","institution_ids":["https://openalex.org/I9694494"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102853310"],"corresponding_institution_ids":["https://openalex.org/I9694494"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12667671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"6","first_page":"730","last_page":"745"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9968000054359436,"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":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6645064949989319},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.6353516578674316},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6126123070716858},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5556094646453857},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5403135418891907},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4511439800262451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3860412538051605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11947998404502869},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08983680605888367}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6645064949989319},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.6353516578674316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6126123070716858},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5556094646453857},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5403135418891907},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4511439800262451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3860412538051605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11947998404502869},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08983680605888367},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/15472450.2021.1974857","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2021.1974857","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"},{"id":"pmh:oai:orbi.umons.ac.be:20.500.12907/42415","is_oa":true,"landing_page_url":"https://orbi.umons.ac.be/handle/20.500.12907/42415","pdf_url":"https://orbi.umons.ac.be/bitstream/20.500.12907/42415/1/Metaheuristic_enabled_deep_convolutional_neural_network_for_traffic_flow_prediction_Impact_of_improved_lion_algorithm.pdf","source":{"id":"https://openalex.org/S7407055454","display_name":"ORBi UMONS","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Intelligent Transportation Systems Research, 25 (5) (2021-08-06)","raw_type":"peer reviewed"},{"id":"mag:3197184836","is_oa":false,"landing_page_url":"https://di.umons.ac.be/details.aspx?pub=5f1fa7cc-49da-450a-b17f-f788f618b284","pdf_url":null,"source":{"id":"https://openalex.org/S118678737","display_name":"International Journal of Intelligent Transportation Systems Research","issn_l":"1348-8503","issn":["1348-8503","1868-8659"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"International Journal of Intelligent Transportation Systems Research","raw_type":null}],"best_oa_location":{"id":"pmh:oai:orbi.umons.ac.be:20.500.12907/42415","is_oa":true,"landing_page_url":"https://orbi.umons.ac.be/handle/20.500.12907/42415","pdf_url":"https://orbi.umons.ac.be/bitstream/20.500.12907/42415/1/Metaheuristic_enabled_deep_convolutional_neural_network_for_traffic_flow_prediction_Impact_of_improved_lion_algorithm.pdf","source":{"id":"https://openalex.org/S7407055454","display_name":"ORBi UMONS","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Intelligent Transportation Systems Research, 25 (5) (2021-08-06)","raw_type":"peer reviewed"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3197184836.pdf","grobid_xml":"https://content.openalex.org/works/W3197184836.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W2049952439","https://openalex.org/W2119460626","https://openalex.org/W2121571815","https://openalex.org/W2159058417","https://openalex.org/W2327031957","https://openalex.org/W2413688291","https://openalex.org/W2507178530","https://openalex.org/W2525614189","https://openalex.org/W2546350184","https://openalex.org/W2551688277","https://openalex.org/W2793820729","https://openalex.org/W2808721728","https://openalex.org/W2889230014","https://openalex.org/W2891064068","https://openalex.org/W2892302657","https://openalex.org/W2896314053","https://openalex.org/W2899443545","https://openalex.org/W2900068794","https://openalex.org/W2904262414","https://openalex.org/W2906154449","https://openalex.org/W2907228515","https://openalex.org/W2924028299","https://openalex.org/W2933410704","https://openalex.org/W2939224083","https://openalex.org/W2939251941","https://openalex.org/W2944240183","https://openalex.org/W2944434139","https://openalex.org/W2963096623","https://openalex.org/W2969797946","https://openalex.org/W2974087501","https://openalex.org/W2975262648","https://openalex.org/W2987635415","https://openalex.org/W2988815247","https://openalex.org/W2997359192","https://openalex.org/W2998374233","https://openalex.org/W2998416884","https://openalex.org/W3001437801"],"related_works":["https://openalex.org/W3197779704","https://openalex.org/W2947327518","https://openalex.org/W2764195644","https://openalex.org/W2166765733","https://openalex.org/W2952355076","https://openalex.org/W2903775874","https://openalex.org/W3161393127","https://openalex.org/W2982309812","https://openalex.org/W2994900513","https://openalex.org/W3010118086","https://openalex.org/W3040421830","https://openalex.org/W3186245354","https://openalex.org/W1997340673","https://openalex.org/W2757185615","https://openalex.org/W3131310266","https://openalex.org/W2085740461","https://openalex.org/W2904346290","https://openalex.org/W2053844988","https://openalex.org/W3178269927","https://openalex.org/W2899070517"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,61,85],"prediction":[2,45,71,81,174],"is":[3,160],"a":[4,136],"basic":[5],"aspect":[6],"to":[7,48,68,77,101],"be":[8],"considered":[9],"in":[10,34,50,55,168],"transportation":[11],"management":[12],"and":[13,20,30,52,118,162,173],"modeling.":[14],"Attaining":[15],"precise":[16,44,126],"information":[17,62],"on":[18,83],"near":[19],"current":[21],"traffic":[22,60,84],"flows":[23],"has":[24],"an":[25,79],"extensive":[26],"range":[27],"of":[28,120,130,157,170],"appliances":[29],"it":[31],"further":[32],"aids":[33],"managing":[35],"the":[36,58,69,97,102,128,153,155,165],"congestion.":[37],"Numerous":[38],"conventional":[39,166],"models":[40,167],"failed":[41],"at":[42],"offering":[43],"results":[46],"due":[47],"\u201cshallow":[49],"architecture":[51],"hand":[53],"engineered":[54],"features\u201d.":[56],"Moreover,":[57,124],"raw":[59],"contains":[63],"noise":[64],"that":[65],"might":[66],"lead":[67],"worst":[70],"results.":[72],"Therefore,":[73],"this":[74],"paper":[75],"intends":[76],"design":[78],"enhanced":[80],"model":[82],"using":[86,135],"Optimized":[87],"Deep":[88],"Convolutional":[89],"Neural":[90],"Network":[91],"(DCNN).":[92],"The":[93],"input":[94],"features":[95],"or":[96],"technical":[98],"indicators":[99],"subjected":[100],"optimized":[103],"CNN":[104],"are":[105,132],"Average":[106,112],"True":[107],"Range":[108],"(ATR),":[109],"Exponential":[110],"Moving":[111],"(EMA),":[113],"Relative":[114],"Strength":[115],"Indicator":[116],"(RSI)":[117],"Rate":[119],"Change":[121],"(ROC),":[122],"respectively.":[123],"for":[125],"prediction,":[127],"weights":[129],"DCNN":[131],"optimally":[133],"tuned":[134],"new":[137],"Improved":[138],"Lion":[139,144],"Algorithm":[140],"(LA)":[141],"termed":[142],"as":[143],"with":[145],"New":[146],"Territorial":[147],"Takeover":[148],"Update":[149],"(LN-TU)":[150],"model.":[151],"In":[152],"end,":[154],"betterment":[156],"implemented":[158],"work":[159],"compared":[161],"proved":[163],"over":[164],"terms":[169],"error":[171],"analysis":[172],"analysis.":[175]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
