{"id":"https://openalex.org/W2343567063","doi":"https://doi.org/10.1109/tits.2016.2515105","title":"High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction","display_name":"High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction","publication_year":2016,"publication_date":"2016-02-19","ids":{"openalex":"https://openalex.org/W2343567063","doi":"https://doi.org/10.1109/tits.2016.2515105","mag":"2343567063"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2016.2515105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2016.2515105","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on 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/A5065846668","display_name":"Jing Zhao","orcid":"https://orcid.org/0000-0003-0158-5330"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Zhao","raw_affiliation_strings":["Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047846625","display_name":"Shiliang Sun","orcid":"https://orcid.org/0000-0001-7069-3752"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiliang Sun","raw_affiliation_strings":["Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065846668"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":6.9046,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.96420329,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"17","issue":"7","first_page":"2014","last_page":"2019"},"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/T12095","display_name":"Vehicle emissions and performance","score":0.9930999875068665,"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"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.991599977016449,"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/gaussian-process","display_name":"Gaussian process","score":0.6751405596733093},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.6021865606307983},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5962435007095337},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5595793724060059},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5496129989624023},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5458141565322876},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5256432294845581},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5147359371185303},{"id":"https://openalex.org/keywords/microscopic-traffic-flow-model","display_name":"Microscopic traffic flow model","score":0.4963236451148987},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4515232443809509},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.44062721729278564},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.41969582438468933},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.37061595916748047},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.33198803663253784},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30215775966644287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2817694842815399},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2798173725605011},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19940370321273804},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16006332635879517},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.1314786970615387}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6751405596733093},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.6021865606307983},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5962435007095337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5595793724060059},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5496129989624023},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5458141565322876},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5256432294845581},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5147359371185303},{"id":"https://openalex.org/C205269179","wikidata":"https://www.wikidata.org/wiki/Q17144160","display_name":"Microscopic traffic flow model","level":3,"score":0.4963236451148987},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4515232443809509},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.44062721729278564},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.41969582438468933},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.37061595916748047},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.33198803663253784},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30215775966644287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2817694842815399},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2798173725605011},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19940370321273804},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16006332635879517},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.1314786970615387},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2016.2515105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2016.2515105","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1378775681","display_name":null,"funder_award_id":"61370175","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1580148563","https://openalex.org/W1982978808","https://openalex.org/W1991515142","https://openalex.org/W1994377164","https://openalex.org/W2016006837","https://openalex.org/W2032678363","https://openalex.org/W2090192376","https://openalex.org/W2097443710","https://openalex.org/W2097498150","https://openalex.org/W2109915598","https://openalex.org/W2114975872","https://openalex.org/W2121313689","https://openalex.org/W2124609748","https://openalex.org/W2133747588","https://openalex.org/W2141948130","https://openalex.org/W2145039203","https://openalex.org/W2160299137","https://openalex.org/W2163769158","https://openalex.org/W2166063021","https://openalex.org/W2166988467","https://openalex.org/W2169779569","https://openalex.org/W2235185765","https://openalex.org/W2357641710","https://openalex.org/W4206030159","https://openalex.org/W6674683336","https://openalex.org/W6678186859","https://openalex.org/W6684451488","https://openalex.org/W6684785420","https://openalex.org/W6689268086"],"related_works":["https://openalex.org/W1985514205","https://openalex.org/W2168894229","https://openalex.org/W3013834874","https://openalex.org/W2052374615","https://openalex.org/W1993282341","https://openalex.org/W2067207793","https://openalex.org/W3156248881","https://openalex.org/W1531820580","https://openalex.org/W2060562186","https://openalex.org/W2375885262"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,7,26,57,95],"prediction,":[2],"which":[3,50],"predicts":[4],"the":[5,31,40,60,64,74,80,91,93,99,102,106,116],"future":[6,94],"using":[8],"historic":[9],"flows,":[10],"is":[11,51,67,77,96],"an":[12],"important":[13],"task":[14],"in":[15,63,79],"intelligent":[16],"transportation":[17],"systems":[18],"(ITS).":[19],"Efficient":[20],"and":[21,73,109,121],"accurate":[22],"models":[23],"for":[24,54,86],"traffic":[25,56],"prediction":[27,126],"greatly":[28],"contribute":[29],"to":[30,46,82],"development":[32],"of":[33,101,125],"ITS.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"adopt":[39],"Gaussian":[41,71],"process":[42],"dynamical":[43],"model":[44,81],"(GPDM)":[45],"a":[47,68],"fourth-order":[48,65,69,107],"GPDM,":[49],"more":[52],"suitable":[53],"modeling":[55],"data.":[58],"Specifically,":[59],"latent":[61,84],"variables":[62,85],"GPDM":[66,108],"Markov":[70],"process,":[72],"weighted":[75],"k-NN":[76],"incorporated":[78],"predict":[83],"efficient":[87],"prediction.":[88],"After":[89],"training":[90],"model,":[92],"estimated":[97],"by":[98,105],"average":[100],"results":[103],"predicted":[104],"k-NN.":[110],"Compared":[111],"with":[112],"other":[113],"popular":[114],"methods,":[115],"proposed":[117],"method":[118],"performs":[119],"best":[120],"yields":[122],"significant":[123],"improvements":[124],"performance.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
