{"id":"https://openalex.org/W2988987604","doi":"https://doi.org/10.1145/3357384.3357867","title":"Forecasting Pavement Performance with a Feature Fusion LSTM-BPNN Model","display_name":"Forecasting Pavement Performance with a Feature Fusion LSTM-BPNN Model","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2988987604","doi":"https://doi.org/10.1145/3357384.3357867","mag":"2988987604"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357867","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","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/A5047581320","display_name":"Yushun Dong","orcid":"https://orcid.org/0000-0001-7504-6159"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yushun Dong","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014615052","display_name":"Yingxia Shao","orcid":"https://orcid.org/0000-0002-8559-2628"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingxia Shao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100371878","display_name":"Xiaotong Li","orcid":"https://orcid.org/0000-0002-7942-5743"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotong Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023133193","display_name":"Sili Li","orcid":"https://orcid.org/0000-0002-5040-8923"},"institutions":[{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]},{"id":"https://openalex.org/I4210139553","display_name":"Research Institute of Highway","ror":"https://ror.org/0335kqk33","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216","https://openalex.org/I4210139553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sili Li","raw_affiliation_strings":["Research Institute of Highway, Ministry of Transportation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Research Institute of Highway, Ministry of Transportation, Beijing, China","institution_ids":["https://openalex.org/I4210139553","https://openalex.org/I4210127216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101737280","display_name":"Lei Quan","orcid":"https://orcid.org/0000-0001-9713-0650"},"institutions":[{"id":"https://openalex.org/I4210139553","display_name":"Research Institute of Highway","ror":"https://ror.org/0335kqk33","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216","https://openalex.org/I4210139553"]},{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Quan","raw_affiliation_strings":["Research Institute of Highway, Ministry of Transportation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Research Institute of Highway, Ministry of Transportation, Beijing, China","institution_ids":["https://openalex.org/I4210139553","https://openalex.org/I4210127216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101561578","display_name":"Zhang We","orcid":"https://orcid.org/0000-0001-6478-3110"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100663187","display_name":"Junping Du","orcid":"https://orcid.org/0000-0001-8590-3767"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junping Du","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5047581320"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.6494,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.83241015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1953","last_page":"1962"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9993000030517578,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9951000213623047,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7414078712463379},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.6780188679695129},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6213529109954834},{"id":"https://openalex.org/keywords/international-roughness-index","display_name":"International Roughness Index","score":0.6140792369842529},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5669091939926147},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5359479188919067},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47716063261032104},{"id":"https://openalex.org/keywords/pavement-management","display_name":"Pavement management","score":0.46500810980796814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4430847764015198},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4162522256374359},{"id":"https://openalex.org/keywords/surface-finish","display_name":"Surface finish","score":0.30331552028656006},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16697418689727783},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.11756914854049683},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06687700748443604}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7414078712463379},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.6780188679695129},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6213529109954834},{"id":"https://openalex.org/C2781212230","wikidata":"https://www.wikidata.org/wiki/Q6052822","display_name":"International Roughness Index","level":3,"score":0.6140792369842529},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5669091939926147},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5359479188919067},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47716063261032104},{"id":"https://openalex.org/C2780996376","wikidata":"https://www.wikidata.org/wiki/Q1747460","display_name":"Pavement management","level":2,"score":0.46500810980796814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4430847764015198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4162522256374359},{"id":"https://openalex.org/C71039073","wikidata":"https://www.wikidata.org/wiki/Q3439090","display_name":"Surface finish","level":2,"score":0.30331552028656006},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16697418689727783},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.11756914854049683},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06687700748443604},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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:10.1145/3357384.3357867","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W21006490","https://openalex.org/W381538111","https://openalex.org/W560384117","https://openalex.org/W582700502","https://openalex.org/W1261917744","https://openalex.org/W1571401318","https://openalex.org/W1994167151","https://openalex.org/W2003409057","https://openalex.org/W2031713099","https://openalex.org/W2050823375","https://openalex.org/W2064675550","https://openalex.org/W2096039506","https://openalex.org/W2124776405","https://openalex.org/W2512971201","https://openalex.org/W2547032137","https://openalex.org/W2565516711","https://openalex.org/W2582664940","https://openalex.org/W2605191235","https://openalex.org/W2734986640","https://openalex.org/W2765131567","https://openalex.org/W2784046873","https://openalex.org/W2805564340","https://openalex.org/W2890167402","https://openalex.org/W2893772597","https://openalex.org/W2901917507","https://openalex.org/W2936766908","https://openalex.org/W2949732208","https://openalex.org/W2954558120"],"related_works":["https://openalex.org/W2171512655","https://openalex.org/W99611931","https://openalex.org/W2973011457","https://openalex.org/W2734775882","https://openalex.org/W2088292687","https://openalex.org/W4224238911","https://openalex.org/W159059482","https://openalex.org/W2106054292","https://openalex.org/W2032228679","https://openalex.org/W2338971571"],"abstract_inverted_index":{"In":[0,109],"modern":[1],"pavement":[2,5,12,20,36,39,68,79],"management":[3,69],"systems,":[4],"roughness":[6,34],"is":[7,26],"an":[8,150],"important":[9],"indicator":[10],"of":[11,19,35,48,55,60,89,102],"performance,":[13],"and":[14,71,92,120,136],"it":[15,72,145],"reflects":[16],"the":[17,27,33,46,53,67,74,78,103,114,118,134,163],"smoothness":[18],"surface.":[21,37],"International":[22],"Roughness":[23],"Index":[24],"(IRI)":[25],"de-facto":[28],"metric":[29],"to":[30,111],"quantitatively":[31],"analyze":[32],"The":[38],"with":[40,139],"high":[41],"IRI":[42,61],"not":[43],"only":[44,98],"reduces":[45],"lifetime":[47],"vehicles,":[49],"but":[50],"also":[51],"raises":[52],"risk":[54],"car":[56],"accidents.":[57],"Accurate":[58],"prediction":[59],"becomes":[62],"a":[63,125,156],"key":[64],"task":[65],"for":[66],"system,":[70],"helps":[73],"transportation":[75],"department":[76],"refurbish":[77],"in":[80],"time.":[81],"However,":[82],"existing":[83,167],"models":[84],"are":[85],"proposed":[86],"on":[87,155],"top":[88],"small":[90],"datasets,":[91],"have":[93],"poor":[94],"performance.":[95],"Besides,":[96],"they":[97],"consider":[99],"cross-sectional":[100,119,135],"features":[101,138,148],"pavements":[104],"without":[105],"any":[106],"time-series":[107,121,137],"information.":[108],"order":[110],"better":[112],"capture":[113],"latent":[115],"relationship":[116],"between":[117],"features,":[122],"we":[123],"propose":[124],"novel":[126],"feature":[127],"fusion":[128],"LSTM-BPNN":[129,131],"model.":[130],"first":[132],"learns":[133],"two":[140],"neural":[141],"networks":[142],"separately,":[143],"then":[144],"fuses":[146],"both":[147],"via":[149],"attention":[151],"mechanism.":[152],"Experimental":[153],"results":[154],"high-quality":[157],"real-world":[158],"dataset":[159],"clearly":[160],"demonstrate":[161],"that":[162],"new":[164],"model":[165],"outperforms":[166],"considerable":[168],"alternatives.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}