{"id":"https://openalex.org/W3161325456","doi":"https://doi.org/10.1109/icassp39728.2021.9414054","title":"FMA-ETA: Estimating Travel Time Entirely Based on FFN with Attention","display_name":"FMA-ETA: Estimating Travel Time Entirely Based on FFN with Attention","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161325456","doi":"https://doi.org/10.1109/icassp39728.2021.9414054","mag":"3161325456"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5103099397","display_name":"Yiwen Sun","orcid":"https://orcid.org/0000-0003-1014-4545"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiwen Sun","raw_affiliation_strings":["Tsinghua University, Beijing, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, P.R.China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100682368","display_name":"Yulu Wang","orcid":"https://orcid.org/0000-0003-0877-5903"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulu Wang","raw_affiliation_strings":["Tsinghua University, Beijing, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, P.R.China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112365916","display_name":"Kun Fu","orcid":"https://orcid.org/0000-0002-0450-6469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Fu","raw_affiliation_strings":["DiDi AI Labs, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, P.R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023289847","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0002-2391-5372"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["DiDi AI Labs, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, P.R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002572395","display_name":"Ziang Yan","orcid":"https://orcid.org/0000-0003-1941-3537"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziang Yan","raw_affiliation_strings":["Tsinghua University, Beijing, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, P.R.China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, P.R.China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010419481","display_name":"Jieping Ye","orcid":"https://orcid.org/0000-0001-8662-5818"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jieping Ye","raw_affiliation_strings":["DiDi AI Labs, Beijing, P.R. China","University of Michigan, Ann Arbor, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, P.R. China","institution_ids":[]},{"raw_affiliation_string":"University of Michigan, Ann Arbor, United States","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3355","last_page":"3359"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","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/T10698","display_name":"Transportation Planning and Optimization","score":0.9986000061035156,"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/inference","display_name":"Inference","score":0.7856377363204956},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.711894690990448},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6872931122779846},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.6250904202461243},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5917130708694458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5529812574386597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4749106168746948},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.47084805369377136},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4577054977416992},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45209628343582153},{"id":"https://openalex.org/keywords/travel-time","display_name":"Travel time","score":0.41358354687690735},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3746801018714905},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13795652985572815},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12786263227462769}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7856377363204956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711894690990448},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6872931122779846},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.6250904202461243},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5917130708694458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5529812574386597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4749106168746948},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.47084805369377136},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4577054977416992},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45209628343582153},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.41358354687690735},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3746801018714905},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13795652985572815},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12786263227462769},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W628224726","https://openalex.org/W1485981043","https://openalex.org/W1514535095","https://openalex.org/W1522301498","https://openalex.org/W1902237438","https://openalex.org/W2073209910","https://openalex.org/W2075364600","https://openalex.org/W2077963913","https://openalex.org/W2110485445","https://openalex.org/W2128084896","https://openalex.org/W2133564696","https://openalex.org/W2136939460","https://openalex.org/W2144475703","https://openalex.org/W2163605009","https://openalex.org/W2548696570","https://openalex.org/W2788997482","https://openalex.org/W2798858969","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2910952060","https://openalex.org/W2911291251","https://openalex.org/W2919115771","https://openalex.org/W2946200149","https://openalex.org/W2962834725","https://openalex.org/W2963367478","https://openalex.org/W2963403868","https://openalex.org/W2964121744","https://openalex.org/W2964198573","https://openalex.org/W2964308564","https://openalex.org/W2965832807","https://openalex.org/W2970730223","https://openalex.org/W2998704965","https://openalex.org/W4254816979","https://openalex.org/W4285719527","https://openalex.org/W4295253143","https://openalex.org/W4385245566","https://openalex.org/W6619990189","https://openalex.org/W6630875275","https://openalex.org/W6679434410","https://openalex.org/W6680230698","https://openalex.org/W6680532216","https://openalex.org/W6684191040","https://openalex.org/W6739901393","https://openalex.org/W6751097180","https://openalex.org/W6758423016","https://openalex.org/W6763832098"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Estimated":[0],"time":[1],"of":[2,7,136],"arrival":[3],"(ETA)":[4],"is":[5,59,70,105,129],"one":[6],"the":[8,45,55,115,122,137],"most":[9],"important":[10],"services":[11],"in":[12,26,134],"intelligent":[13],"transportation":[14],"systems":[15],"(ITS)":[16],"and":[17,53,64,84,113],"becomes":[18],"a":[19,81],"challenging":[20],"spatial-temporal":[21],"(ST)":[22],"data":[23,50],"mining":[24],"task":[25],"recent":[27],"years.":[28],"Nowadays,":[29],"deep":[30],"learning":[31],"based":[32,39,88],"methods,":[33],"specifically":[34],"recurrent":[35],"neural":[36],"networks":[37],"(RNN)":[38],"ones":[40],"are":[41],"adapted":[42],"to":[43,72,107],"model":[44],"ST":[46],"patterns":[47],"from":[48,61],"massive":[49],"for":[51,93],"ETA":[52],"become":[54],"state-of-the-art.":[56],"However,":[57],"RNN":[58],"suffering":[60],"slow":[62],"training":[63],"inference":[65,143],"speed,":[66],"as":[67],"its":[68],"structure":[69],"unfriendly":[71],"parallel":[73],"computing.":[74],"To":[75],"solve":[76],"this":[77],"problem,":[78],"we":[79],"propose":[80],"novel,":[82],"brief":[83],"effective":[85],"framework":[86],"mainly":[87],"on":[89,121],"feed-forward":[90],"network":[91],"(FFN)":[92],"ETA,":[94],"FFN":[95],"with":[96,109,131,140],"Multifactor":[97],"Attention":[98,103],"(FMA-ETA).":[99],"The":[100],"novel":[101],"Multi-factor":[102],"mechanism":[104],"proposed":[106],"deal":[108],"different":[110],"category":[111],"features":[112],"aggregate":[114],"information":[116],"purposefully.":[117],"Extensive":[118],"experimental":[119],"results":[120],"real-world":[123],"vehicle":[124],"travel":[125],"dataset":[126],"show":[127],"FMA-ETA":[128],"competitive":[130],"state-of-the-art":[132],"methods":[133],"terms":[135],"prediction":[138],"accuracy":[139],"significantly":[141],"better":[142],"speed.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}