{"id":"https://openalex.org/W3197872836","doi":"https://doi.org/10.1109/mt-its49943.2021.9529336","title":"A Gaussian Mixture Model and Data Fusion Approach for Urban Travel Time Forecast","display_name":"A Gaussian Mixture Model and Data Fusion Approach for Urban Travel Time Forecast","publication_year":2021,"publication_date":"2021-06-16","ids":{"openalex":"https://openalex.org/W3197872836","doi":"https://doi.org/10.1109/mt-its49943.2021.9529336","mag":"3197872836"},"language":"en","primary_location":{"id":"doi:10.1109/mt-its49943.2021.9529336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its49943.2021.9529336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","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/A5022911422","display_name":"Andrea Gemma","orcid":"https://orcid.org/0000-0002-6177-382X"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Andrea Gemma","raw_affiliation_strings":["Roma Tre University,Department of Engineering,Rome,Italy"],"affiliations":[{"raw_affiliation_string":"Roma Tre University,Department of Engineering,Rome,Italy","institution_ids":["https://openalex.org/I119003972"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002518807","display_name":"Livia Mannini","orcid":"https://orcid.org/0000-0002-5021-7094"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Livia Mannini","raw_affiliation_strings":["Roma Tre University,Department of Engineering,Rome,Italy"],"affiliations":[{"raw_affiliation_string":"Roma Tre University,Department of Engineering,Rome,Italy","institution_ids":["https://openalex.org/I119003972"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015033342","display_name":"Stefano Carrese","orcid":"https://orcid.org/0000-0002-4810-5093"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefano Carrese","raw_affiliation_strings":["Roma Tre University,Department of Engineering,Rome,Italy"],"affiliations":[{"raw_affiliation_string":"Roma Tre University,Department of Engineering,Rome,Italy","institution_ids":["https://openalex.org/I119003972"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007521351","display_name":"Ernesto Cipriani","orcid":"https://orcid.org/0000-0002-5073-9378"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ernesto Cipriani","raw_affiliation_strings":["Roma Tre University,Department of Engineering,Rome,Italy"],"affiliations":[{"raw_affiliation_string":"Roma Tre University,Department of Engineering,Rome,Italy","institution_ids":["https://openalex.org/I119003972"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002864424","display_name":"Umberto Crisalli","orcid":"https://orcid.org/0000-0002-2672-8054"},"institutions":[{"id":"https://openalex.org/I119003972","display_name":"Roma Tre University","ror":"https://ror.org/05vf0dg29","country_code":"IT","type":"education","lineage":["https://openalex.org/I119003972"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Umberto Crisalli","raw_affiliation_strings":["Roma Tre University,Department of Engineering,Rome,Italy"],"affiliations":[{"raw_affiliation_string":"Roma Tre University,Department of Engineering,Rome,Italy","institution_ids":["https://openalex.org/I119003972"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5022911422"],"corresponding_institution_ids":["https://openalex.org/I119003972"],"apc_list":null,"apc_paid":null,"fwci":0.3559,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.58565064,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9958000183105469,"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":0.9915000200271606,"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":0.6784080862998962},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5962634682655334},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.567708432674408},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5079827904701233},{"id":"https://openalex.org/keywords/real-time-data","display_name":"Real-time data","score":0.506588339805603},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4886644184589386},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4567315876483917},{"id":"https://openalex.org/keywords/goodness-of-fit","display_name":"Goodness of fit","score":0.43638136982917786},{"id":"https://openalex.org/keywords/bluetooth","display_name":"Bluetooth","score":0.42112886905670166},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41001808643341064},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3270798921585083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20875772833824158},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18685591220855713},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10252025723457336}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6784080862998962},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5962634682655334},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.567708432674408},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5079827904701233},{"id":"https://openalex.org/C110593043","wikidata":"https://www.wikidata.org/wiki/Q7300787","display_name":"Real-time data","level":2,"score":0.506588339805603},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4886644184589386},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4567315876483917},{"id":"https://openalex.org/C132480984","wikidata":"https://www.wikidata.org/wiki/Q2034239","display_name":"Goodness of fit","level":2,"score":0.43638136982917786},{"id":"https://openalex.org/C546215728","wikidata":"https://www.wikidata.org/wiki/Q39531","display_name":"Bluetooth","level":3,"score":0.42112886905670166},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41001808643341064},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3270798921585083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20875772833824158},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18685591220855713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10252025723457336},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/mt-its49943.2021.9529336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its49943.2021.9529336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","raw_type":"proceedings-article"},{"id":"pmh:oai:art.torvergata.it:2108/322783","is_oa":false,"landing_page_url":"https://hdl.handle.net/2108/322783","pdf_url":null,"source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/bookPart"},{"id":"pmh:oai:iris.uniroma3.it:11590/405820","is_oa":false,"landing_page_url":"http://hdl.handle.net/11590/405820","pdf_url":null,"source":{"id":"https://openalex.org/S4377196120","display_name":"Iris (Roma Tre University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I119003972","host_organization_name":"Roma Tre University","host_organization_lineage":["https://openalex.org/I119003972"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8299999833106995,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W603924357","https://openalex.org/W1513927654","https://openalex.org/W1561128958","https://openalex.org/W1602211094","https://openalex.org/W1852328876","https://openalex.org/W1970574049","https://openalex.org/W1975086685","https://openalex.org/W1976333578","https://openalex.org/W1976495514","https://openalex.org/W2001355817","https://openalex.org/W2004831545","https://openalex.org/W2011504567","https://openalex.org/W2027665506","https://openalex.org/W2056145546","https://openalex.org/W2074108366","https://openalex.org/W2076246037","https://openalex.org/W2085866051","https://openalex.org/W2089079865","https://openalex.org/W2100366979","https://openalex.org/W2127012233","https://openalex.org/W2133177820","https://openalex.org/W2157409281","https://openalex.org/W2163565319","https://openalex.org/W2165327689","https://openalex.org/W2169560049","https://openalex.org/W2189058472","https://openalex.org/W2190353863","https://openalex.org/W2217607432","https://openalex.org/W2239838331","https://openalex.org/W2269049861","https://openalex.org/W2302719757","https://openalex.org/W2319145018","https://openalex.org/W2346039921","https://openalex.org/W2543610789","https://openalex.org/W2556487516","https://openalex.org/W2593149854","https://openalex.org/W2736186863","https://openalex.org/W2779684064","https://openalex.org/W2781864963","https://openalex.org/W2798987131","https://openalex.org/W2807691880","https://openalex.org/W2883345462","https://openalex.org/W2884860764","https://openalex.org/W2905108066","https://openalex.org/W3012477623","https://openalex.org/W3129093284","https://openalex.org/W4244532478","https://openalex.org/W4285719527","https://openalex.org/W6618457977","https://openalex.org/W6650501041","https://openalex.org/W6683788084","https://openalex.org/W6687168902","https://openalex.org/W6693705418","https://openalex.org/W6750374569"],"related_works":["https://openalex.org/W2511870814","https://openalex.org/W2389206316","https://openalex.org/W2322628425","https://openalex.org/W2108644170","https://openalex.org/W2027286771","https://openalex.org/W3200288307","https://openalex.org/W2843407882","https://openalex.org/W2023772872","https://openalex.org/W2015673430","https://openalex.org/W3197872836"],"abstract_inverted_index":{"Travel":[0],"time":[1,99,111,134],"information":[2,76],"is":[3,93,107,129],"gaining":[4],"increasing":[5],"importance":[6],"as":[7],"traffic":[8,19,32,53],"performance":[9,159],"measure":[10],"from":[11],"the":[12,29,71,74,97,132,144,149,152],"perspective":[13],"of":[14,31,73,79,146,151],"both":[15],"drivers":[16],"to":[17,24,69,95,138],"understand":[18],"conditions":[20],"and":[21,27,59,85,100,105,113],"network":[22,158],"management":[23],"properly":[25],"monitor":[26],"control":[28],"evolution":[30],"conditions.":[33],"Aiming":[34],"at":[35],"forecasting":[36],"path":[37],"travel":[38,98,133],"times":[39],"in":[40,51,143],"congested":[41],"urban":[42],"areas,":[43],"this":[44],"paper":[45],"proposes":[46],"a":[47,80,86,139],"data-":[48],"driven":[49],"approach":[50,154],"which":[52],"data":[54,66,88,118],"are":[55,67],"collected":[56],"via":[57],"Bluetooth":[58],"mobile":[60],"Floating":[61],"Car":[62],"Data":[63],"devices.":[64],"Such":[65],"used":[68,130],"improve":[70],"accuracy":[72],"detected":[75],"by":[77],"means":[78],"Gaussian":[81],"Mixture":[82],"Model":[83],"(GMM)":[84],"Bayesian":[87],"fusion":[89],"approach.":[90],"The":[91],"GMM":[92],"applied":[94],"estimate":[96],"not":[101],"only":[102],"its":[103],"distribution":[104],"it":[106],"calibrated":[108],"for":[109,131,155],"each":[110],"interval":[112],"updated":[114],"with":[115],"every":[116],"available":[117],"on":[119],"real":[120],"time.":[121],"An":[122,136],"Auto-":[123],"Regressive":[124],"Integrated":[125],"Moving":[126],"Average":[127],"model":[128],"forecast.":[135,160],"application":[137],"real-life":[140],"test":[141],"case":[142],"city":[145],"Rome":[147],"shows":[148],"goodness":[150],"proposed":[153],"better":[156],"online":[157]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-09-13T00:00:00"}