{"id":"https://openalex.org/W3112483519","doi":"https://doi.org/10.1109/tits.2020.3040386","title":"CoDriver ETA: Combine Driver Information in Estimated Time of Arrival by Driving Style Learning Auxiliary Task","display_name":"CoDriver ETA: Combine Driver Information in Estimated Time of Arrival by Driving Style Learning Auxiliary Task","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3112483519","doi":"https://doi.org/10.1109/tits.2020.3040386","mag":"3112483519"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2020.3040386","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3040386","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/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":["Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1014-4545","affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101956475","display_name":"Kun Fu","orcid":"https://orcid.org/0000-0002-2305-1017"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Fu","raw_affiliation_strings":["DiDi AI Labs, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401173","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0003-4873-505X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["DiDi AI Labs, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001517167","display_name":"Donghua Zhou","orcid":"https://orcid.org/0000-0003-0169-8490"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]},{"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":"Donghua Zhou","raw_affiliation_strings":["Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0003-0169-8490","affiliations":[{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062033891","display_name":"Kailun Wu","orcid":"https://orcid.org/0000-0003-1424-392X"},"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":"Kailun Wu","raw_affiliation_strings":["Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1424-392X","affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","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, China","Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","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":["Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8088-367X","affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence, Tsinghua University (THUAI), Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7866,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.83348953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"23","issue":"5","first_page":"4037","last_page":"4048"},"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":0.9997000098228455,"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":0.9988999962806702,"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":0.7335461974143982},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6993811726570129},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.573209285736084},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5614369511604309},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5473126173019409},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.5411412715911865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5379219055175781},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49511972069740295},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.418630987405777},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4167076349258423},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1389981508255005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7335461974143982},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6993811726570129},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.573209285736084},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5614369511604309},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5473126173019409},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.5411412715911865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5379219055175781},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49511972069740295},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.418630987405777},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4167076349258423},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1389981508255005},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2020.3040386","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.3040386","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":[{"id":"https://metadata.un.org/sdg/10","score":0.5699999928474426,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G6887114618","display_name":null,"funder_award_id":"61751307","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7341942736","display_name":null,"funder_award_id":"2018AAA0100701","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W34239198","https://openalex.org/W1875842236","https://openalex.org/W1977594918","https://openalex.org/W2064675550","https://openalex.org/W2073209910","https://openalex.org/W2075364600","https://openalex.org/W2077963913","https://openalex.org/W2096733369","https://openalex.org/W2108196201","https://openalex.org/W2139137304","https://openalex.org/W2140833774","https://openalex.org/W2144475703","https://openalex.org/W2165698076","https://openalex.org/W2165991108","https://openalex.org/W2168791259","https://openalex.org/W2194775991","https://openalex.org/W2295739661","https://openalex.org/W2399456070","https://openalex.org/W2535805784","https://openalex.org/W2548696570","https://openalex.org/W2774231091","https://openalex.org/W2788997482","https://openalex.org/W2796096089","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2884609870","https://openalex.org/W2903871660","https://openalex.org/W2913340405","https://openalex.org/W2919115771","https://openalex.org/W2945622688","https://openalex.org/W2962917186","https://openalex.org/W2964098640","https://openalex.org/W2965832807","https://openalex.org/W2972581457","https://openalex.org/W2997993234","https://openalex.org/W2998704965","https://openalex.org/W3025104845","https://openalex.org/W3106295757","https://openalex.org/W3160775000","https://openalex.org/W6601402213","https://openalex.org/W6619990189","https://openalex.org/W6631190155","https://openalex.org/W6680532216","https://openalex.org/W6680887930","https://openalex.org/W6776418668"],"related_works":["https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W2081900870","https://openalex.org/W1582019636","https://openalex.org/W1499005795","https://openalex.org/W3172493050","https://openalex.org/W4385420271","https://openalex.org/W4312192618","https://openalex.org/W2593798266","https://openalex.org/W4385414918"],"abstract_inverted_index":{"Estimated":[0],"time":[1,60],"of":[2,7,23,42,80,128,145,153,188,211],"arrival":[3],"(ETA)":[4],"is":[5],"one":[6],"the":[8,45,72,129,143,150,161,169,175,185,189,218],"most":[9],"important":[10],"services":[11],"in":[12,44,90,174],"intelligent":[13],"transportation":[14],"systems":[15],"(ITS).":[16],"Precise":[17],"ETA":[18],"ensures":[19],"proper":[20],"travel":[21,190,214],"scheduling":[22],"passengers":[24],"as":[25,27,75],"well":[26,70],"guarantees":[28],"efficient":[29],"decision-making":[30],"on":[31,196,209],"ride-hailing":[32],"platforms,":[33],"which":[34],"are":[35,78],"used":[36],"by":[37,171],"an":[38,121,126],"explosively":[39],"growing":[40],"number":[41],"people":[43],"past":[46],"few":[47],"years.":[48],"Recently,":[49],"machine":[50],"learning-based":[51],"methods":[52,99],"have":[53,86],"been":[54],"widely":[55],"adopted":[56],"to":[57,115,124,167],"solve":[58,116],"this":[59,106,117,138,157],"estimation":[61],"problem":[62,96],"and":[63,83],"become":[64],"state-of-the-art.":[65],"However,":[66],"they":[67],"do":[68,84],"not":[69,85],"explore":[71],"personalization":[73],"information,":[74],"many":[76],"drivers":[77],"short":[79],"personalized":[81,130],"data":[82,89,94,215],"sufficient":[87],"trajectory":[88],"real":[91],"applications.":[92],"This":[93,178],"sparsity":[95],"prevents":[97],"existing":[98],"from":[100,164,201],"obtaining":[101],"higher":[102],"prediction":[103,186],"accuracy.":[104],"In":[105,137],"article,":[107],"we":[108,140,159],"propose":[109],"a":[110],"novel":[111],"deep":[112],"learning":[113,135],"method":[114,195,220],"problem.":[118],"We":[119,192],"introduce":[120],"auxiliary":[122],"task":[123],"learn":[125,142,168],"embedding":[127,144,170,181],"driving":[131,146,154],"information":[132],"under":[133],"multi-task":[134],"framework.":[136],"task,":[139],"discriminatively":[141],"preference":[147],"that":[148,217],"preserves":[149],"historical":[151,212],"statistics":[152],"speed.":[155],"For":[156],"purpose,":[158],"adapt":[160],"triplet":[162],"network":[163],"face":[165],"recognition":[166],"constructing":[172],"triplets":[173],"feature":[176],"space.":[177],"simultaneously":[179],"learned":[180],"can":[182],"effectively":[183],"boost":[184],"accuracy":[187],"time.":[191],"evaluate":[193],"our":[194],"two":[197],"large-scale":[198],"real-world":[199],"datasets":[200],"Didi":[202],"Chuxing":[203],"platform.":[204],"The":[205],"extensive":[206],"experimental":[207],"results":[208],"billions":[210],"vehicle":[213],"demonstrate":[216],"proposed":[219],"outperforms":[221],"state-of-the-art":[222],"algorithms.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}