{"id":"https://openalex.org/W2321968596","doi":"https://doi.org/10.1080/15472450.2016.1149700","title":"Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model","display_name":"Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model","publication_year":2016,"publication_date":"2016-02-20","ids":{"openalex":"https://openalex.org/W2321968596","doi":"https://doi.org/10.1080/15472450.2016.1149700","mag":"2321968596"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2016.1149700","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2016.1149700","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-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/A5032029351","display_name":"Wenxin Qiao","orcid":"https://orcid.org/0000-0003-0586-6244"},"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":"Wenxin Qiao","raw_affiliation_strings":["MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091159879","display_name":"Ali Haghani","orcid":"https://orcid.org/0000-0003-3181-7155"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Haghani","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101959797","display_name":"Chunfu Shao","orcid":"https://orcid.org/0000-0002-8047-6898"},"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":false,"raw_author_name":"Chun-Fu Shao","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014627828","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0003-3242-3182"},"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":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032029351"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":3.0491,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.8992402,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"20","issue":"5","first_page":"438","last_page":"448"},"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":0.9993000030517578,"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":0.9937000274658203,"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/travel-time","display_name":"Travel time","score":0.6311348676681519},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6069169044494629},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5777750015258789},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5114985704421997},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.5054023265838623},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.47833868861198425},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4643023610115051},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.4204634130001068},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3395211100578308},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32317107915878296},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2369873821735382},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1465590000152588},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14415764808654785},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07323896884918213}],"concepts":[{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.6311348676681519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6069169044494629},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5777750015258789},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5114985704421997},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.5054023265838623},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.47833868861198425},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4643023610115051},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.4204634130001068},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3395211100578308},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32317107915878296},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2369873821735382},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1465590000152588},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14415764808654785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07323896884918213},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2016.1149700","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2016.1149700","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W71105111","https://openalex.org/W180185937","https://openalex.org/W626441390","https://openalex.org/W651330626","https://openalex.org/W1605343822","https://openalex.org/W1621267340","https://openalex.org/W1840508907","https://openalex.org/W1988489815","https://openalex.org/W1989982053","https://openalex.org/W2003069895","https://openalex.org/W2007051754","https://openalex.org/W2007215240","https://openalex.org/W2007526069","https://openalex.org/W2011285426","https://openalex.org/W2024558842","https://openalex.org/W2049964304","https://openalex.org/W2059832076","https://openalex.org/W2072293025","https://openalex.org/W2073640212","https://openalex.org/W2082533141","https://openalex.org/W2090192376","https://openalex.org/W2097498150","https://openalex.org/W2103400647","https://openalex.org/W2133177820","https://openalex.org/W2168332608","https://openalex.org/W2169424991","https://openalex.org/W2245253600","https://openalex.org/W2516321972","https://openalex.org/W4242502692"],"related_works":["https://openalex.org/W2807758032","https://openalex.org/W4224254130","https://openalex.org/W2152103536","https://openalex.org/W3048948123","https://openalex.org/W413879896","https://openalex.org/W1983530038","https://openalex.org/W2972374246","https://openalex.org/W4243114048","https://openalex.org/W2331293474","https://openalex.org/W4323568965"],"abstract_inverted_index":{"Providing":[0],"reliable":[1],"travel":[2,17,33],"time":[3,18,34],"prediction":[4,35,69],"is":[5,51],"very":[6],"much":[7],"needed":[8],"for":[9,11,29,80],"commuters":[10],"their":[12],"upcoming":[13],"trips":[14],"to":[15],"reduce":[16],"and":[19,31,41,45],"relieve":[20],"traffic":[21,44,59],"congestion.":[22],"This":[23],"article":[24],"proposes":[25],"an":[26],"integrated":[27],"model":[28,66],"path":[30],"multi-step-ahead":[32],"on":[36],"freeways":[37],"using":[38],"both":[39],"historical":[40,93],"real-time":[42],"heterogeneous":[43],"weather":[46],"data.":[47],"The":[48],"model's":[49,83],"performance":[50,73],"investigated":[52],"in":[53,71],"a":[54],"case":[55],"study":[56],"under":[57],"various":[58,72],"scenarios.":[60],"Results":[61],"indicate":[62],"that":[63],"the":[64,82,89],"proposed":[65],"provides":[67],"satisfactory":[68],"results":[70],"tests.":[74],"For":[75],"practical":[76],"purposes,":[77],"general":[78],"guidelines":[79],"selecting":[81],"parameter":[84],"sets":[85],"as":[86,88],"well":[87],"efficient":[90],"size":[91],"of":[92],"data":[94],"are":[95],"also":[96],"presented.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}