{"id":"https://openalex.org/W2765912720","doi":"https://doi.org/10.1145/3144789.3144804","title":"On-line Multi-step Prediction of Short Term Traffic Flow Based on GRU Neural Network","display_name":"On-line Multi-step Prediction of Short Term Traffic Flow Based on GRU Neural Network","publication_year":2017,"publication_date":"2017-07-17","ids":{"openalex":"https://openalex.org/W2765912720","doi":"https://doi.org/10.1145/3144789.3144804","mag":"2765912720"},"language":"en","primary_location":{"id":"doi:10.1145/3144789.3144804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3144789.3144804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","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/A5061634365","display_name":"Jingyan Guo","orcid":"https://orcid.org/0009-0001-0449-8366"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyan Guo","raw_affiliation_strings":["School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027514004","display_name":"Zijun Wang","orcid":"https://orcid.org/0000-0002-2410-9460"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijun Wang","raw_affiliation_strings":["School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100704485","display_name":"Huawei Chen","orcid":"https://orcid.org/0000-0002-5020-3012"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Chen","raw_affiliation_strings":["School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics &amp; Astronautics, University of Electronic Science and Technology of China, ChengDu, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4153,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.8262182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9947999715805054,"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":0.9914000034332275,"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/computer-science","display_name":"Computer science","score":0.7233176231384277},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6823120713233948},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.6363241672515869},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5496257543563843},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4361559748649597},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.43285924196243286},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.42773962020874023},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.42029261589050293},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.41206520795822144},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3408401608467102},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3266042172908783},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3235119879245758}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233176231384277},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6823120713233948},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.6363241672515869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5496257543563843},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4361559748649597},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.43285924196243286},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.42773962020874023},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.42029261589050293},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.41206520795822144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3408401608467102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3266042172908783},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3235119879245758},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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:10.1145/3144789.3144804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3144789.3144804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6800000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W581956982","https://openalex.org/W1508856789","https://openalex.org/W2049500727","https://openalex.org/W2059128538","https://openalex.org/W2064675550","https://openalex.org/W2090192376","https://openalex.org/W2094520581","https://openalex.org/W2107878631","https://openalex.org/W2136848157","https://openalex.org/W2157331557","https://openalex.org/W2255466643","https://openalex.org/W2341808896","https://openalex.org/W2573587735","https://openalex.org/W2586779249","https://openalex.org/W3023883182"],"related_works":["https://openalex.org/W2587362999","https://openalex.org/W432084041","https://openalex.org/W2963251637","https://openalex.org/W2986732134","https://openalex.org/W2394010358","https://openalex.org/W2361078351","https://openalex.org/W2052374615","https://openalex.org/W4239349137","https://openalex.org/W1463884142","https://openalex.org/W239469043"],"abstract_inverted_index":{"Strengthened":[0],"road":[1,9,14,35],"traffic":[2,10,15,26,36,134],"flow":[3,27,37],"monitoring":[4,32],"and":[5,12,42,71,99,105],"forecasting":[6],"can":[7,125],"ease":[8],"congestion":[11],"facilitate":[13],"safety":[16],"planning.":[17],"Multi-step":[18],"ahead":[19],"of":[20,34,88,115],"the":[21,25,46,83,86,89,102,112,116],"ability":[22],"to":[23,49,96],"predict":[24],"is":[28,38,109,121],"particularly":[29],"important.":[30],"The":[31,123],"data":[33],"characterized":[39],"by":[40],"uncertainty":[41],"non-linearity.":[43],"And":[44],"using":[45],"existing":[47],"methods":[48],"carry":[50],"out":[51],"multi-step":[52,75,133],"prediction":[53,113,119],"error":[54],"will":[55],"be":[56,126],"very":[57],"large.":[58],"In":[59],"this":[60,79],"paper,":[61],"based":[62],"on":[63],"these":[64],"feature,":[65],"we":[66],"propose":[67],"GRU":[68,118],"neural":[69],"network":[70,84],"autocorrelation":[72],"analysis":[73],"for":[74,132],"prediction.":[76,135],"We":[77],"make":[78],"model":[80,120,124],"dynamically":[81],"update":[82],"with":[85],"input":[87],"measured":[90],"real-time":[91],"data,":[92],"namely":[93],"on-line":[94],"prediction,":[95],"work":[97],"effectively":[98],"constantly.":[100],"Through":[101],"theoretical":[103],"derivation":[104],"simulation":[106],"analysis,":[107],"it":[108],"shown":[110],"that":[111],"accuracy":[114],"proposed":[117],"improved.":[122],"used":[127],"as":[128],"an":[129],"effective":[130],"method":[131]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
