{"id":"https://openalex.org/W2081117251","doi":"https://doi.org/10.1109/itsc.2013.6728236","title":"A spatio-temporal multivariate adaptive regression splines approach for short-term freeway traffic volume prediction","display_name":"A spatio-temporal multivariate adaptive regression splines approach for short-term freeway traffic volume prediction","publication_year":2013,"publication_date":"2013-10-01","ids":{"openalex":"https://openalex.org/W2081117251","doi":"https://doi.org/10.1109/itsc.2013.6728236","mag":"2081117251"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2013.6728236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2013.6728236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","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/A5100704962","display_name":"Yanyan Xu","orcid":"https://orcid.org/0000-0001-5429-3177"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanyan Xu","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, China","[Department of Automation, Shanghai Jiao Tong University, Shanghai, China.]"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, China","institution_ids":[]},{"raw_affiliation_string":"[Department of Automation, Shanghai Jiao Tong University, Shanghai, China.]","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101735181","display_name":"Qing\u2010Jie Kong","orcid":"https://orcid.org/0000-0002-3788-305X"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing-Jie Kong","raw_affiliation_strings":["State Key Laboratory for Management, Institute of Automation, Beijing, China","State Key Lab. for Manage. & Control of Complex Syst., Inst. of Autom., Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Management, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]},{"raw_affiliation_string":"State Key Lab. for Manage. & Control of Complex Syst., Inst. of Autom., Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061110835","display_name":"Yuncai Liu","orcid":"https://orcid.org/0000-0002-4040-4478"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuncai Liu","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, China","[Department of Automation, Shanghai Jiao Tong University, Shanghai, China.]"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, China","institution_ids":[]},{"raw_affiliation_string":"[Department of Automation, Shanghai Jiao Tong University, Shanghai, China.]","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100704962"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.465,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72747822,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1644","issue":null,"first_page":"217","last_page":"222"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9939000010490417,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.8499995470046997},{"id":"https://openalex.org/keywords/mars-exploration-program","display_name":"Mars Exploration Program","score":0.6959595084190369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6121396422386169},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5534539222717285},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5464928150177002},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5283133387565613},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5248498916625977},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5224094390869141},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.44497597217559814},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4198642075061798},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3828548192977905},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3734651505947113},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.34210240840911865},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24697986245155334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1965552568435669},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14676284790039062}],"concepts":[{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.8499995470046997},{"id":"https://openalex.org/C83260615","wikidata":"https://www.wikidata.org/wiki/Q6773121","display_name":"Mars Exploration Program","level":2,"score":0.6959595084190369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121396422386169},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5534539222717285},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5464928150177002},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5283133387565613},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5248498916625977},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5224094390869141},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.44497597217559814},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4198642075061798},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3828548192977905},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3734651505947113},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.34210240840911865},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24697986245155334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1965552568435669},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14676284790039062},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"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:10.1109/itsc.2013.6728236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2013.6728236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W601070381","https://openalex.org/W2007062458","https://openalex.org/W2016210396","https://openalex.org/W2024558842","https://openalex.org/W2051087393","https://openalex.org/W2085592822","https://openalex.org/W2090192376","https://openalex.org/W2091859074","https://openalex.org/W2094909852","https://openalex.org/W2102201073","https://openalex.org/W2122127011","https://openalex.org/W2131767615","https://openalex.org/W2133747588","https://openalex.org/W2150010190","https://openalex.org/W2165137963","https://openalex.org/W2546465629","https://openalex.org/W6652333024"],"related_works":["https://openalex.org/W4256152544","https://openalex.org/W1481829876","https://openalex.org/W2181828400","https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2146461990","https://openalex.org/W4312309719","https://openalex.org/W2264001480"],"abstract_inverted_index":{"Current":[0],"freeway":[1,34,91,110],"traffic":[2,68,79,147],"flow":[3],"prediction":[4,11,22,46,132,162],"techniques":[5],"pay":[6],"attention":[7],"to":[8,64,121,134],"time":[9],"series":[10,85],"or":[12],"introduce":[13],"the":[14,20,29,33,40,45,59,66,71,75,98,101,106,109,117,122,130,136,145,150,154,167,173,177],"upstream":[15],"adjacent":[16],"road":[17,30,60],"segments":[18,31],"in":[19,92,164],"short-term":[21,67,137],"model.":[23,47],"In":[24,97],"this":[25],"paper,":[26],"all":[27],"of":[28,39,86,105],"on":[32,74,108,144],"are":[35,81,111,125,141],"considered":[36],"as":[37],"candidates":[38],"independent":[41],"variables":[42],"fed":[43,128],"into":[44,129],"A":[48],"spatio-temporal":[49,156],"multivariate":[50],"adaptive":[51],"regression":[52],"splines":[53],"(MARS)":[54],"approach":[55],"is":[56],"proposed":[57,155],"for":[58],"network":[61],"analysis":[62],"and":[63,127,149,176],"predict":[65],"volume":[69],"at":[70],"observation":[72,87],"stations":[73,88,107,118],"freeway.":[76],"The":[77,139],"actual":[78,146],"data":[80,148,169],"collected":[82],"from":[83],"a":[84,90],"along":[89],"Portland":[93],"every":[94],"15":[95],"minutes.":[96],"first":[99],"phase,":[100],"macroscopic":[102],"dependency":[103],"relationships":[104],"investigated":[112],"via":[113],"MARS":[114,131,157,171],"method.":[115],"Subsequently":[116],"most":[119],"related":[120],"object":[123],"station":[124],"selected":[126],"model":[133,158],"generate":[135,160],"volume.":[138],"experiments":[140],"carried":[142],"out":[143],"results":[151],"indicate":[152],"that":[153],"can":[159],"superior":[161],"accuracy":[163],"contrast":[165],"with":[166],"historical":[168],"based":[170],"model,":[172],"parametric":[174],"ARIMA,":[175],"nonparametric":[178],"PPR":[179],"methods.":[180]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
