{"id":"https://openalex.org/W3131157223","doi":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348555","title":"Dynamic Hidden Markov Model for Metropolitan Traffic Flow Prediction","display_name":"Dynamic Hidden Markov Model for Metropolitan Traffic Flow Prediction","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3131157223","doi":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348555","mag":"3131157223"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2020-fall49728.2020.9348555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)","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/A5100672514","display_name":"Zihan Li","orcid":"https://orcid.org/0009-0004-3839-0611"},"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":"Zihan Li","raw_affiliation_strings":["Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038267080","display_name":"Cailian Chen","orcid":"https://orcid.org/0000-0001-6533-8713"},"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":"Cailian Chen","raw_affiliation_strings":["Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081235198","display_name":"Min Yang","orcid":"https://orcid.org/0000-0003-1973-527X"},"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":"Yang Min","raw_affiliation_strings":["Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004668792","display_name":"Jianping He","orcid":"https://orcid.org/0000-0002-6253-7802"},"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":"Jianping He","raw_affiliation_strings":["Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080458204","display_name":"Bo Yang","orcid":"https://orcid.org/0000-0001-8726-4890"},"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":"Bo Yang","raw_affiliation_strings":["Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Ministry of Education of China, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Shanghai, P. R. China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9965000152587891,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6540653109550476},{"id":"https://openalex.org/keywords/metropolitan-area","display_name":"Metropolitan area","score":0.5935885906219482},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5387540459632874},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.5300913453102112},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5027482509613037},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46716248989105225},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4208894371986389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.246830016374588},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1992860734462738},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.17444172501564026},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1315089464187622},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11403831839561462},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09829634428024292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6540653109550476},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.5935885906219482},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5387540459632874},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5300913453102112},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5027482509613037},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46716248989105225},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4208894371986389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.246830016374588},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1992860734462738},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.17444172501564026},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1315089464187622},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11403831839561462},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09829634428024292},{"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/vtc2020-fall49728.2020.9348555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-fall49728.2020.9348555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W1970851960","https://openalex.org/W1984969638","https://openalex.org/W2002033255","https://openalex.org/W2145039203","https://openalex.org/W2166771065","https://openalex.org/W2307933510","https://openalex.org/W2530386080","https://openalex.org/W2583466634","https://openalex.org/W2592824913","https://openalex.org/W2593182953","https://openalex.org/W2615477033","https://openalex.org/W2795138333","https://openalex.org/W2808862972","https://openalex.org/W3102406289","https://openalex.org/W6619978402"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2379651310","https://openalex.org/W2082284720","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697","https://openalex.org/W2194396582","https://openalex.org/W2027903142","https://openalex.org/W2116722627"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,48,140],"prediction":[2,141],"is":[3,74,88],"one":[4],"of":[5,39,94,129],"the":[6,22,85,92,101,113,122,127,134],"core":[7],"technologies":[8],"in":[9,19],"Intelligent":[10],"Transportation":[11],"System":[12],"(ITS)":[13],"to":[14,45,66,90,110],"improve":[15],"traffic":[16,24,30,47,81,95,114,130,139,148],"management.":[17],"However,":[18],"metropolitan":[20],"circumstances,":[21],"complex":[23],"road":[25],"networks":[26],"and":[27,42,107,133,146],"numerous":[28],"unpredictable":[29],"anomalies":[31,43,96,108,131],"are":[32],"still":[33],"tough":[34],"problems,":[35],"which":[36,84],"bring":[37],"challenges":[38],"leveraging":[40],"topological":[41,106],"information":[44,109],"accurate":[46],"prediction.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53],"propose":[54],"a":[55],"Dynamic":[56],"Hidden":[57],"Markov":[58],"Model":[59],"(DHMM)":[60],"based":[61],"on":[62,117],"global":[63,71],"PageRank":[64,72,86,102,123],"algorithm":[65,73,79],"overcome":[67],"these":[68],"challenges.":[69],"The":[70],"more":[75],"applicable":[76],"than":[77],"traditional":[78],"for":[80],"scenarios,":[82],"through":[83],"metric":[87,124],"calculated":[89],"measure":[91],"accumulation":[93],"at":[97],"intersections.":[98],"By":[99],"incorporating":[100],"metric,":[103],"DHMM":[104],"leverages":[105],"dynamically":[111],"model":[112,136],"variations.":[115],"Experiments":[116],"real-world":[118],"dataset":[119],"demonstrate":[120],"that":[121],"can":[125],"describe":[126],"degree":[128],"intuitively,":[132],"proposed":[135],"has":[137],"superior":[138],"performance":[142],"both":[143],"under":[144],"normal":[145],"abnormal":[147],"conditions.":[149]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
