{"id":"https://openalex.org/W2207466483","doi":"https://doi.org/10.1109/dsp-spe.2015.7369592","title":"Traffic flow forecasting research based on Bayesian normalized Elman neural network","display_name":"Traffic flow forecasting research based on Bayesian normalized Elman neural network","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2207466483","doi":"https://doi.org/10.1109/dsp-spe.2015.7369592","mag":"2207466483"},"language":"en","primary_location":{"id":"doi:10.1109/dsp-spe.2015.7369592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsp-spe.2015.7369592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)","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/A5068185012","display_name":"Wenchi Ma","orcid":"https://orcid.org/0000-0003-1323-4298"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenchi Ma","raw_affiliation_strings":["Dept. of Information Engineering, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100777473","display_name":"Ruijie Wang","orcid":"https://orcid.org/0000-0001-9611-4974"},"institutions":[{"id":"https://openalex.org/I7350606","display_name":"Dalian Jiaotong University","ror":"https://ror.org/05gp45n31","country_code":"CN","type":"education","lineage":["https://openalex.org/I7350606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruijie Wang","raw_affiliation_strings":["Dept. of Electronic Information Engineering, Dalian Jiaotong University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Information Engineering, Dalian Jiaotong University, Dalian, China","institution_ids":["https://openalex.org/I7350606"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068185012"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.7562,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77169135,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"18b","issue":null,"first_page":"426","last_page":"430"},"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":0.9909999966621399,"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/T10524","display_name":"Traffic control and management","score":0.9848999977111816,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.8340898752212524},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.677668571472168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6039336323738098},{"id":"https://openalex.org/keywords/probabilistic-neural-network","display_name":"Probabilistic neural network","score":0.5995538234710693},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5759702324867249},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49586448073387146},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.49171048402786255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4890521466732025},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.463293194770813},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.43748903274536133},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4325335919857025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08648449182510376}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.8340898752212524},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.677668571472168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6039336323738098},{"id":"https://openalex.org/C134342201","wikidata":"https://www.wikidata.org/wiki/Q7246859","display_name":"Probabilistic neural network","level":4,"score":0.5995538234710693},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5759702324867249},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49586448073387146},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.49171048402786255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4890521466732025},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.463293194770813},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.43748903274536133},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4325335919857025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08648449182510376},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsp-spe.2015.7369592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsp-spe.2015.7369592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)","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":3,"referenced_works":["https://openalex.org/W180185937","https://openalex.org/W2090192376","https://openalex.org/W4300402905"],"related_works":["https://openalex.org/W2390121886","https://openalex.org/W1595652908","https://openalex.org/W2375446027","https://openalex.org/W2007798284","https://openalex.org/W2067837718","https://openalex.org/W2089093251","https://openalex.org/W38478948","https://openalex.org/W4385506173","https://openalex.org/W4294967761","https://openalex.org/W2179098615"],"abstract_inverted_index":{"In":[0],"this":[1],"thesis,":[2],"a":[3,17,52,133],"single,":[4],"separate":[5],"section,":[6],"for":[7,55,149],"example,":[8],"is":[9,26,51,83,146],"used":[10],"to":[11,85,131],"forecast":[12],"the":[13,38,62,69,74,87,90,95,100,104],"traffic":[14,110,152],"flow":[15],"in":[16,33],"long":[18,150],"time.":[19],"The":[20],"advantage":[21],"of":[22,29,78,89,103,113],"artificial":[23,48],"neural":[24,49,66,80,115,121,144],"network":[25,39,50,67,81,145],"its":[27],"ability":[28,102],"learning":[30],"or":[31],"training":[32],"other":[34],"words.":[35],"By":[36],"learning,":[37],"can":[40],"give":[41],"appropriate":[42],"output":[43],"when":[44],"accepting":[45],"input.":[46],"Thus,":[47],"good":[53],"model":[54,71],"predicting":[56],"transportation":[57],"flow.":[58],"This":[59],"paper":[60],"proposes":[61],"Bayesian":[63,138],"normalized":[64,139],"Elman":[65,79,143],"as":[68],"prediction":[70,96],"which":[72,98],"has":[73],"reliability":[75],"and":[76,82,126],"stability":[77],"able":[84],"overcome":[86],"influence":[88],"hidden":[91],"layer":[92],"nodes":[93],"on":[94,108,142],"accuracy,":[97],"improves":[99],"generalization":[101],"network.":[105],"Then":[106],"depending":[107],"long-time":[109],"forecasting":[111],"results":[112],"different":[114],"networks":[116],"like":[117],"classical":[118],"BP,":[119],"wavelet":[120],"network,":[122],"statistics":[123],"accuracy":[124],"error":[125],"comparative":[127],"analysis":[128],"are":[129],"finished":[130],"draw":[132],"conclusion":[134],"that":[135],"combined":[136],"with":[137],"method":[140],"based":[141],"more":[147],"suitable":[148],"time":[151],"forecast.":[153]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
