{"id":"https://openalex.org/W4394870847","doi":"https://doi.org/10.1145/3650400.3650485","title":"Highway pavement temperature short-term prediction model based on multi-layer GRU","display_name":"Highway pavement temperature short-term prediction model based on multi-layer GRU","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4394870847","doi":"https://doi.org/10.1145/3650400.3650485"},"language":"en","primary_location":{"id":"doi:10.1145/3650400.3650485","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","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/A5027753920","display_name":"Wen Long Li","orcid":"https://orcid.org/0009-0004-2085-8511"},"institutions":[{"id":"https://openalex.org/I4210130035","display_name":"China Communications Construction Company (China)","ror":"https://ror.org/03q3een69","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210130035"]},{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen Li","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, China, \rNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China and \rYunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, China, \rNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China and \rYunnan Key Laboratory of Digital Communications, Yunnan Communications Investment & Construction Group Co., Ltd, China","institution_ids":["https://openalex.org/I4210130035","https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028535547","display_name":"Xingtong Chen","orcid":"https://orcid.org/0009-0006-3115-3538"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xingtong Chen","raw_affiliation_strings":["National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China"],"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034834857","display_name":"Xuekun Yang","orcid":"https://orcid.org/0000-0003-2355-7082"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuekun Yang","raw_affiliation_strings":["National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China"],"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100778603","display_name":"Dan Xu","orcid":"https://orcid.org/0000-0003-4602-3550"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Xu","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027753920"],"corresponding_institution_ids":["https://openalex.org/I189210763","https://openalex.org/I4210130035"],"apc_list":null,"apc_paid":null,"fwci":0.0752,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34218066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"516","last_page":"522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12682","display_name":"Smart Materials for Construction","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12682","display_name":"Smart Materials for Construction","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.7343787550926208},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5961220860481262},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5719419717788696},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.5537956357002258},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.541946530342102},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5050221085548401},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.46272578835487366},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.4385870099067688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43355610966682434},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.41639474034309387},{"id":"https://openalex.org/keywords/winter-storm","display_name":"Winter storm","score":0.41488611698150635},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.30391961336135864},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29537639021873474},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.23577550053596497},{"id":"https://openalex.org/keywords/storm","display_name":"Storm","score":0.21456199884414673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1533222496509552},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14008745551109314},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08997765183448792}],"concepts":[{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.7343787550926208},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5961220860481262},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5719419717788696},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.5537956357002258},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.541946530342102},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5050221085548401},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.46272578835487366},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.4385870099067688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43355610966682434},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.41639474034309387},{"id":"https://openalex.org/C196937547","wikidata":"https://www.wikidata.org/wiki/Q75079023","display_name":"Winter storm","level":3,"score":0.41488611698150635},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.30391961336135864},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29537639021873474},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.23577550053596497},{"id":"https://openalex.org/C105306849","wikidata":"https://www.wikidata.org/wiki/Q81054","display_name":"Storm","level":2,"score":0.21456199884414673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1533222496509552},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14008745551109314},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08997765183448792},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3650400.3650485","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5600000023841858,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W852276632","https://openalex.org/W1549342387","https://openalex.org/W2624087348","https://openalex.org/W2972423093","https://openalex.org/W3159807327","https://openalex.org/W3165686696","https://openalex.org/W4225856991","https://openalex.org/W4284977297"],"related_works":["https://openalex.org/W3121369812","https://openalex.org/W3178576217","https://openalex.org/W4316658904","https://openalex.org/W4304606463","https://openalex.org/W2933969434","https://openalex.org/W4200374151","https://openalex.org/W4247388746","https://openalex.org/W2625413331","https://openalex.org/W2087911819","https://openalex.org/W3121540092"],"abstract_inverted_index":{"The":[0],"extreme":[1,54,212],"temperatures":[2,110],"experienced":[3],"by":[4,124],"highway":[5,52,87,121,213],"pavements":[6],"significantly":[7],"impact":[8,104],"road":[9,71],"traffic,":[10],"primarily":[11],"stemming":[12],"from":[13,119],"meteorological":[14,37,106,116],"disasters":[15],"such":[16],"as":[17],"unusual":[18],"high":[19],"air":[20],"temperatures,":[21,89],"snowstorms,":[22],"and":[23,42,73,111,147,170,197],"freezing":[24],"conditions.":[25],"However,":[26],"existing":[27],"approaches":[28],"fail":[29],"to":[30],"adequately":[31],"account":[32],"for":[33,48,70,86],"the":[34,103,125,150,178,204],"influence":[35],"of":[36,51,105,130,189],"factors.":[38],"Developing":[39],"a":[40,92,120,156],"rational":[41],"effective":[43],"modeling":[44],"approach":[45],"is":[46,112],"crucial":[47],"precise":[49],"predictions":[50,57],"pavement":[53,88,109,214],"temperatures.":[55,215],"Such":[56],"not":[58],"only":[59],"help":[60],"prevent":[61],"traffic":[62,74],"accidents":[63],"but":[64],"also":[65],"offer":[66],"reliable":[67],"decision-making":[68],"support":[69],"maintenance":[72],"management.":[75],"In":[76],"this":[77,208],"research,":[78],"we":[79],"present":[80],"an":[81],"efficient":[82],"short-term":[83],"prediction":[84,144,158],"model":[85,100],"based":[90],"on":[91,108],"multi-layered":[93],"Gated":[94],"Recurrent":[95],"Unit":[96],"(GRU)":[97],"framework.":[98],"This":[99],"comprehensively":[101],"incorporates":[102],"factors":[107],"subsequently":[113],"validated":[114],"using":[115],"data":[117],"collected":[118],"section":[122],"managed":[123],"Zhaotong":[126],"District":[127],"Operation":[128],"Department":[129],"Yunnan":[131],"Communications":[132],"Investment":[133],"&":[134],"Construction":[135],"Group":[136],"CO.,":[137],"LTD.":[138],"We":[139],"conduct":[140],"comparisons":[141],"across":[142],"various":[143],"time":[145],"intervals":[146],"find":[148],"that":[149,182,188,203],"model's":[151,185],"optimal":[152],"performance":[153,186],"occurs":[154],"at":[155],"1.5-hour":[157],"step,":[159],"with":[160],"lowest":[161],"Mean":[162,166,171],"Absolute":[163,172],"Error":[164,168,174],"(MAE),":[165],"Squared":[167],"(MSE),":[169],"Percentage":[173],"(MAPE)":[175],"values.":[176],"Moreover,":[177],"experimental":[179,200],"outcomes":[180],"indicate":[181],"our":[183],"proposed":[184,206],"outperforms":[187],"other":[190],"competing":[191],"models,":[192],"including":[193],"BiGRU-attention,":[194],"BiLSTM-attention,":[195],"Muti-RNN,":[196],"Muti-LSTM.":[198],"Our":[199],"results":[201],"demonstrate":[202],"method":[205],"in":[207],"study":[209],"effectively":[210],"predicts":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}