{"id":"https://openalex.org/W4312241939","doi":"https://doi.org/10.1145/3565291.3565349","title":"Lane Occupancy Prediction: A Deep Model Based on Dilated Convolution and Graph Convolution Fusion","display_name":"Lane Occupancy Prediction: A Deep Model Based on Dilated Convolution and Graph Convolution Fusion","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4312241939","doi":"https://doi.org/10.1145/3565291.3565349"},"language":"en","primary_location":{"id":"doi:10.1145/3565291.3565349","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565291.3565349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data Technologies","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/A5069302982","display_name":"Xiaotong Wang","orcid":"https://orcid.org/0000-0002-4141-8994"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotong Wang","raw_affiliation_strings":["Qingdao University, China"],"raw_orcid":"https://orcid.org/0000-0002-4141-8994","affiliations":[{"raw_affiliation_string":"Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030501441","display_name":"Xiufang Sun","orcid":"https://orcid.org/0000-0002-2453-0300"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiufang Sun","raw_affiliation_strings":["Qingdao University, China"],"raw_orcid":"https://orcid.org/0000-0002-2453-0300","affiliations":[{"raw_affiliation_string":"Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102752513","display_name":"Zesheng Cheng","orcid":"https://orcid.org/0000-0003-4150-1728"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zesheng Cheng","raw_affiliation_strings":["Qingdao University, China"],"raw_orcid":"https://orcid.org/0000-0003-4150-1728","affiliations":[{"raw_affiliation_string":"Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108688024"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"357","last_page":"362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9948999881744385,"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/T12095","display_name":"Vehicle emissions and performance","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/convolution","display_name":"Convolution (computer science)","score":0.7455243468284607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6346388459205627},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5172213315963745},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4771166145801544},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.41978755593299866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3395559787750244},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.33004218339920044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31924325227737427},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1737004816532135},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11019143462181091}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7455243468284607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6346388459205627},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5172213315963745},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4771166145801544},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.41978755593299866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3395559787750244},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.33004218339920044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31924325227737427},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1737004816532135},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11019143462181091}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3565291.3565349","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565291.3565349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1551942241","https://openalex.org/W1996058270","https://openalex.org/W2038206846","https://openalex.org/W2047332899","https://openalex.org/W2057918527","https://openalex.org/W2108196201","https://openalex.org/W2483158495","https://openalex.org/W2525614189","https://openalex.org/W2561406409","https://openalex.org/W2579792847","https://openalex.org/W2887050416","https://openalex.org/W2901504064","https://openalex.org/W2963343478","https://openalex.org/W2964193755","https://openalex.org/W2971269959","https://openalex.org/W3158590963","https://openalex.org/W3176186894","https://openalex.org/W3182305291","https://openalex.org/W3198637603","https://openalex.org/W3199175618","https://openalex.org/W3215509581","https://openalex.org/W4200591625","https://openalex.org/W4205996475","https://openalex.org/W4226288807","https://openalex.org/W4285387016"],"related_works":["https://openalex.org/W2182785089","https://openalex.org/W4312178642","https://openalex.org/W4387838477","https://openalex.org/W2067193074","https://openalex.org/W3107426390","https://openalex.org/W2375093801","https://openalex.org/W4312624109","https://openalex.org/W3175683656","https://openalex.org/W2892553522","https://openalex.org/W4388214549"],"abstract_inverted_index":{"Due":[0],"to":[1,48,62,76],"the":[2,38,50,56,64,97,103,112,118,128,132,143,158,163,168,171,174,183],"growing":[3],"population":[4],"and":[5,13,43,89,117,126,173],"substantially":[6],"increased":[7],"motor":[8],"vehicle":[9,90],"usage,":[10],"traffic":[11,68,81,87,115],"congestion":[12],"insufficient":[14],"allocation":[15],"of":[16,40,67,80,114,120,130,157,167,182],"transportation":[17],"hardware":[18],"resources":[19],"have":[20],"become":[21],"a":[22,33],"very":[23],"serious":[24],"problem.":[25],"To":[26],"solve":[27],"this":[28,30,84,110],"problem,":[29],"work":[31,85],"proposes":[32],"deep":[34],"model":[35,169],"based":[36],"on":[37,142],"fusion":[39],"dilated":[41,71],"convolution":[42,45,58,72],"graph":[44,57],"network":[46],"(DGCN)":[47],"predict":[49],"lane":[51,133],"occupancy":[52,134],"rate.":[53],"Among":[54],"them,":[55],"layer":[59,73],"is":[60,74,136,170,178],"used":[61,75],"extract":[63,77],"spatial":[65],"correlation":[66],"data.":[69,82],"The":[70,155],"temporal":[78],"correlations":[79],"And":[83],"adds":[86],"flow":[88],"speed":[91],"as":[92],"two":[93],"implicit":[94,122],"factors":[95,123],"into":[96],"model,":[98],"which":[99],"are":[100,124,139],"fused":[101],"with":[102,152],"previously":[104],"extracted":[105],"multi":[106],"modal":[107],"features.":[108],"In":[109],"way,":[111],"non-linearity":[113],"data":[116,144],"influence":[119],"many":[121],"solved,":[125],"then":[127],"task":[129],"predicting":[131],"rate":[135,166,177],"completed.":[137],"Experiments":[138],"carried":[140],"out":[141],"set":[145],"published":[146],"by":[147],"California":[148],"Transportation":[149],"Bureau,":[150],"compared":[151],"other":[153],"models.":[154],"analysis":[156],"experimental":[159],"results":[160],"shows":[161],"that":[162,181],"average":[164],"error":[165,175],"lowest":[172],"growth":[176],"lower":[179],"than":[180],"contrast":[184],"model.":[185]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}