{"id":"https://openalex.org/W3208522400","doi":"https://doi.org/10.1145/3459637.3482138","title":"LTPHM: Long-term Traffic Prediction based on Hybrid Model","display_name":"LTPHM: Long-term Traffic Prediction based on Hybrid Model","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3208522400","doi":"https://doi.org/10.1145/3459637.3482138","mag":"3208522400"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482138","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5047764578","display_name":"Chuyin Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuyin Huang","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003299054","display_name":"Weiyang Kong","orcid":"https://orcid.org/0000-0003-0578-2956"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyang Kong","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018994926","display_name":"Genan Dai","orcid":"https://orcid.org/0000-0003-2583-0433"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genan Dai","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101541107","display_name":"Yubao Liu","orcid":"https://orcid.org/0000-0002-9504-1619"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yubao Liu","raw_affiliation_strings":["Sun Yat-Sen University &amp; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University &amp; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3093","last_page":"3097"},"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":0.989799976348877,"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":0.9873999953269958,"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":0.8187065124511719},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.641685962677002},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.5486434698104858},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5229528546333313},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5211629867553711},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.48381584882736206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44436460733413696},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4360889196395874},{"id":"https://openalex.org/keywords/iterative-learning-control","display_name":"Iterative learning control","score":0.4211735129356384},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39601457118988037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38938236236572266},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.194835364818573},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1879599392414093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8187065124511719},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.641685962677002},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.5486434698104858},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5229528546333313},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5211629867553711},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.48381584882736206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44436460733413696},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4360889196395874},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.4211735129356384},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39601457118988037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38938236236572266},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.194835364818573},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1879599392414093},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482138","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W1983883318","https://openalex.org/W2468907370","https://openalex.org/W2903871660","https://openalex.org/W2963358464","https://openalex.org/W2963840672","https://openalex.org/W2964311892","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W3103720336"],"related_works":["https://openalex.org/W2761624296","https://openalex.org/W1674316682","https://openalex.org/W2626546066","https://openalex.org/W2062347313","https://openalex.org/W2498640783","https://openalex.org/W2022562732","https://openalex.org/W1495035728","https://openalex.org/W2034475059","https://openalex.org/W2026748623","https://openalex.org/W2185389722"],"abstract_inverted_index":{"Traffic":[0,83],"prediction":[1,6,15,35,62,109],"is":[2,42,91],"a":[3,76],"classical":[4],"spaial-temporal":[5],"problem":[7],"with":[8,115,126,180],"many":[9],"real-world":[10,182],"applications.In":[11],"general,":[12],"existing":[13,69],"traffic":[14,100,183],"methods":[16],"capture":[17,45,131,150,167],"the":[18,29,34,39,46,66,95,103,108,112,116,156,168,175,186],"complex":[19,169],"spatial-\u00adtemporal":[20,120],"features":[21],"by":[22,106],"iterative":[23,30,160],"mechanism":[24,31,41],"or":[25],"non-iterative":[26,162],"mechanism.":[27],"However,":[28],"often":[32],"causes":[33],"error":[36],"accumulation":[37],"and":[38,142,161,170],"non-\u00aditerative":[40],"hard":[43],"to":[44,56,93,130,149],"dynamic":[47,96,171],"propagation":[48],"information.":[49],"The":[50],"shortcomings":[51,67],"of":[52,68,99,111,158,188],"both":[53,159],"mechanisms":[54],"lead":[55],"their":[57],"poor":[58],"performance":[59],"in":[60,71],"long-\u00adterm":[61],"tasks.":[63],"Target":[64],"at":[65],"methods,":[70],"this":[72],"paper,":[73],"we":[74,135],"propose":[75],"novel":[77],"deep":[78],"learning":[79],"framework":[80],"called":[81],"Long-term":[82],"Prediction":[84],"based":[85],"on":[86,102],"Hybrid":[87],"Model":[88],"(LTPHM),":[89],"which":[90],"designed":[92],"simulate":[94],"transmission":[97],"process":[98],"information":[101],"road":[104],"network":[105],"connecting":[107],"values":[110],"current":[113],"step":[114],"next":[117],"step.":[118],"Each":[119],"module":[121],"uses":[122],"graph":[123],"convolution":[124,146],"(GCN)":[125],"an":[127],"adaptive":[128],"matrix":[129],"spatial":[132],"dependence.":[133,152],"Besides,":[134],"use":[136],"Gated":[137,143],"Dilated":[138],"Convolution":[139],"Networks":[140],"(GDCN)":[141],"Linear":[144],"Unit":[145],"networks":[147],"(GLU)":[148],"temporal":[151,177],"Since":[153],"LTPHM":[154],"integrates":[155],"advantages":[157],"prediction,":[163],"it":[164],"can":[165],"efficiently":[166],"spatial-temporal":[172],"features,":[173],"especially":[174],"long-range":[176],"sequences.":[178],"Experiments":[179],"three":[181],"datasets":[184],"demonstrate":[185],"effectiveness":[187],"our":[189],"proposed":[190],"model.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
