{"id":"https://openalex.org/W3213295382","doi":"https://doi.org/10.1109/mfi52462.2021.9591204","title":"A Convolutional Neural Network Combined with a Gaussian Process for Speed Prediction in Traffic Networks","display_name":"A Convolutional Neural Network Combined with a Gaussian Process for Speed Prediction in Traffic Networks","publication_year":2021,"publication_date":"2021-09-23","ids":{"openalex":"https://openalex.org/W3213295382","doi":"https://doi.org/10.1109/mfi52462.2021.9591204","mag":"3213295382"},"language":"en","primary_location":{"id":"doi:10.1109/mfi52462.2021.9591204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi52462.2021.9591204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5000882682","display_name":"Yifei Zhu","orcid":"https://orcid.org/0000-0003-4352-6507"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yifei Zhu","raw_affiliation_strings":["University of Sheffield, UK"],"affiliations":[{"raw_affiliation_string":"University of Sheffield, UK","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395956","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0001-9895-394X"},"institutions":[{"id":"https://openalex.org/I11983389","display_name":"Manchester Metropolitan University","ror":"https://ror.org/02hstj355","country_code":"GB","type":"education","lineage":["https://openalex.org/I11983389"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["Manchester Metropolitan University, Manchester, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Manchester Metropolitan University, Manchester, United Kingdom","institution_ids":["https://openalex.org/I11983389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006505402","display_name":"Lyudmila Mihaylova","orcid":"https://orcid.org/0000-0001-5856-2223"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Lyudmila Mihaylova","raw_affiliation_strings":["University of Sheffield, UK"],"affiliations":[{"raw_affiliation_string":"University of Sheffield, UK","institution_ids":["https://openalex.org/I91136226"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000882682"],"corresponding_institution_ids":["https://openalex.org/I91136226"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.18310211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"abs 1711 165","issue":null,"first_page":"1","last_page":"7"},"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":0.9984999895095825,"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/T10370","display_name":"Traffic and Road Safety","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.835658609867096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7174100875854492},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6368389129638672},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5966489315032959},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5701749920845032},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5296392440795898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4928129017353058},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48746275901794434},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4852035343647003},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4682881236076355},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.45717886090278625},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.45591673254966736},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.41988441348075867},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4162638187408447},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41394418478012085},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3925107717514038},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3804502785205841},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3218245506286621},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.177200049161911},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17100778222084045},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13458508253097534}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.835658609867096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7174100875854492},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6368389129638672},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5966489315032959},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5701749920845032},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5296392440795898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4928129017353058},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48746275901794434},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4852035343647003},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4682881236076355},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.45717886090278625},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.45591673254966736},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.41988441348075867},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4162638187408447},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41394418478012085},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3925107717514038},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3804502785205841},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3218245506286621},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.177200049161911},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17100778222084045},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13458508253097534},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mfi52462.2021.9591204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi52462.2021.9591204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:178098","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Proceedings Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2141436875","display_name":null,"funder_award_id":"EP/T013265/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W1522301498","https://openalex.org/W1994377164","https://openalex.org/W2007339694","https://openalex.org/W2036785686","https://openalex.org/W2131241448","https://openalex.org/W2182396527","https://openalex.org/W2528639018","https://openalex.org/W2579495707","https://openalex.org/W2605264395","https://openalex.org/W2766678531","https://openalex.org/W2785994986","https://openalex.org/W2807037403","https://openalex.org/W2884604806","https://openalex.org/W2885059312","https://openalex.org/W2898146813","https://openalex.org/W2902865840","https://openalex.org/W2912080346","https://openalex.org/W2963323437","https://openalex.org/W2963703618","https://openalex.org/W2964052793","https://openalex.org/W2964121744","https://openalex.org/W3090811938","https://openalex.org/W6631190155","https://openalex.org/W6678911119","https://openalex.org/W6728547873","https://openalex.org/W6743446608","https://openalex.org/W6745256532","https://openalex.org/W6748053814","https://openalex.org/W6752195321","https://openalex.org/W6755055104"],"related_works":["https://openalex.org/W4389055065","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,9,15,63,79,117],"traffic":[4,37,56,73,94],"speed":[5,74,105],"prediction":[6],"framework":[7,92,98],"combining":[8],"Convolutional":[10],"Neural":[11],"Network":[12],"(CNN)":[13],"with":[14,111],"Gaussian":[16,59],"Process":[17,60],"(G":[18],"P)":[19],"and":[20,39,85,115],"is":[21,32,45,76],"an":[22],"extension":[23],"of":[24,69,89,103,120],"Conv":[25],"N":[26],"et-GP":[27],"[1].":[28],"The":[29,43,58,72,96],"main":[30],"focus":[31],"on":[33,40,46,49,53],"spatio-temporal":[34],"large":[35],"scale":[36],"networks":[38],"uncertainty":[41],"quantification.":[42],"emphasis":[44],"the":[47,50,54,67,70,90,104,112,121],"impact":[48],"measurement":[51],"noises":[52],"predicted":[55],"speeds.":[57],"regression":[61],"provides":[62,99],"variance":[64],"which":[65],"characterises":[66],"accuracy":[68],"prediction.":[71],"data":[75],"converted":[77],"into":[78],"three":[80],"dimensional":[81],"format":[82],"like":[83],"images":[84],"these":[86],"are":[87],"inputs":[88],"CNN-GP":[91,97],"for":[93],"networks.":[95],"18.23%":[100],"average":[101],"improvement":[102],"root":[106],"mean":[107],"square":[108],"error":[109],"compared":[110],"generic":[113],"CNN":[114],"gives":[116],"quantitative":[118],"characterisation":[119],"noise":[122],"effects.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
