{"id":"https://openalex.org/W2105107431","doi":"https://doi.org/10.1109/itsc.2006.1706819","title":"A Combination Forecasting Model of Urban Ring Road Traffic Flow","display_name":"A Combination Forecasting Model of Urban Ring Road Traffic Flow","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2105107431","doi":"https://doi.org/10.1109/itsc.2006.1706819","mag":"2105107431"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2006.1706819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2006.1706819","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE Intelligent Transportation Systems Conference","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/A5108509285","display_name":"Wei Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Guan","raw_affiliation_strings":["Systems Engineering, Traffic and Transportation School of Beijing, Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Systems Engineering, Traffic and Transportation School of Beijing, Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100405604","display_name":"Hua Xie","orcid":"https://orcid.org/0009-0007-7586-9279"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie Hua","raw_affiliation_strings":["Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"Jiaotong University, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108509285"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.6013,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.89803315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"671","last_page":"676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9979000091552734,"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":0.9979000091552734,"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14368","display_name":"Evaluation and Optimization Models","score":0.9484999775886536,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.8315825462341309},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.7436815500259399},{"id":"https://openalex.org/keywords/ring-road","display_name":"Ring road","score":0.7436286211013794},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.6603116393089294},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.6305249929428101},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6257829666137695},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5601280927658081},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.47184374928474426},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.42315444350242615},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4203597903251648},{"id":"https://openalex.org/keywords/traffic-congestion-reconstruction-with-kerners-three-phase-theory","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","score":0.41639846563339233},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3471265733242035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2767646014690399},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.22680741548538208},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21079853177070618},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18026021122932434},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0972621738910675},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08045381307601929}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8315825462341309},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.7436815500259399},{"id":"https://openalex.org/C2778360411","wikidata":"https://www.wikidata.org/wiki/Q510662","display_name":"Ring road","level":2,"score":0.7436286211013794},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.6603116393089294},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6305249929428101},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6257829666137695},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5601280927658081},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.47184374928474426},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.42315444350242615},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4203597903251648},{"id":"https://openalex.org/C25492975","wikidata":"https://www.wikidata.org/wiki/Q960570","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","level":3,"score":0.41639846563339233},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3471265733242035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2767646014690399},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.22680741548538208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21079853177070618},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18026021122932434},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0972621738910675},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08045381307601929},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2006.1706819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2006.1706819","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE Intelligent Transportation Systems Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"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":6,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2132901555","https://openalex.org/W2151067040","https://openalex.org/W2353513180","https://openalex.org/W2369688539","https://openalex.org/W2389913108"],"related_works":["https://openalex.org/W301804496","https://openalex.org/W2042439812","https://openalex.org/W2972320057","https://openalex.org/W2359005881","https://openalex.org/W2973105956","https://openalex.org/W3008657070","https://openalex.org/W1492556197","https://openalex.org/W2897741166","https://openalex.org/W2988005308","https://openalex.org/W4386289889"],"abstract_inverted_index":{"A":[0],"combination":[1,47,73],"forecasting":[2,48,52,74,78,83],"model":[3,18,59,75],"of":[4,29,39],"urban":[5],"ring":[6],"road":[7],"traffic":[8,26,69,86],"flow":[9,27],"based":[10],"on":[11],"neural":[12,61],"network,":[13],"Kalman":[14],"filter":[15],"and":[16,24,60],"ARIMA":[17,58],"is":[19],"proposed":[20],"in":[21,54,67,85],"this":[22],"paper,":[23],"the":[25,37,40,51,68],"data":[28],"Beijing":[30],"third-ring-road":[31],"(BTRR)":[32],"are":[33,64],"explored":[34],"to":[35,56],"test":[36],"validity":[38],"model.":[41],"The":[42],"experimental":[43],"results":[44],"show":[45],"that":[46],"cannot":[49],"improve":[50,77],"precision":[53,79],"contrast":[55],"which":[57],"network":[62],"models":[63,84],"more":[65],"accurate":[66],"congestion":[70],"periods.":[71],"However,":[72],"can":[76],"comparing":[80],"with":[81],"single":[82],"non-congestion":[87],"periods":[88]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}