{"id":"https://openalex.org/W2965725760","doi":"https://doi.org/10.24963/ijcai.2019/234","title":"Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation","display_name":"Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2965725760","doi":"https://doi.org/10.24963/ijcai.2019/234","mag":"2965725760"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/234","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/234","pdf_url":"https://www.ijcai.org/proceedings/2019/0234.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0234.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016181167","display_name":"Ruipeng Gao","orcid":"https://orcid.org/0000-0002-2490-6654"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruipeng Gao","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102892343","display_name":"Xiaoyu Guo","orcid":"https://orcid.org/0000-0002-0901-2222"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Guo","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052867186","display_name":"Fuyong Sun","orcid":"https://orcid.org/0000-0002-7052-7698"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuyong Sun","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001135065","display_name":"Lin Dai","orcid":"https://orcid.org/0000-0001-8258-6727"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Dai","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067524861","display_name":"Jiayan Zhu","orcid":"https://orcid.org/0000-0003-0162-4678"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiayan Zhu","raw_affiliation_strings":["Beijing DiDi Infinity Technology and Development Co., Ltd"],"affiliations":[{"raw_affiliation_string":"Beijing DiDi Infinity Technology and Development Co., Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101430329","display_name":"Chenxi Hu","orcid":"https://orcid.org/0000-0001-7692-358X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenxi Hu","raw_affiliation_strings":["Beijing DiDi Infinity Technology and Development Co., Ltd"],"affiliations":[{"raw_affiliation_string":"Beijing DiDi Infinity Technology and Development Co., Ltd","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100394079","display_name":"Haibo Li","orcid":"https://orcid.org/0000-0002-4060-2167"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haibo Li","raw_affiliation_strings":["Beijing DiDi Infinity Technology and Development Co., Ltd"],"affiliations":[{"raw_affiliation_string":"Beijing DiDi Infinity Technology and Development Co., Ltd","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5016181167"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":3.4158,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.91655757,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1689","last_page":"1696"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994000196456909,"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":0.9969000220298767,"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":0.7278326749801636},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.7101088762283325},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6266321539878845},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.564128041267395},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5275325775146484},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.5183778405189514},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4701262414455414},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.42789697647094727},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4137257933616638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35619425773620605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.346599817276001},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.28318125009536743},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1477205455303192},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10565441846847534}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7278326749801636},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.7101088762283325},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6266321539878845},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.564128041267395},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5275325775146484},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.5183778405189514},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4701262414455414},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.42789697647094727},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4137257933616638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35619425773620605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.346599817276001},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28318125009536743},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1477205455303192},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10565441846847534},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/234","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/234","pdf_url":"https://www.ijcai.org/proceedings/2019/0234.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/234","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/234","pdf_url":"https://www.ijcai.org/proceedings/2019/0234.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5299999713897705}],"awards":[{"id":"https://openalex.org/G1048658767","display_name":null,"funder_award_id":"61702035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2702356746","display_name":null,"funder_award_id":"61702","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2802911279","display_name":null,"funder_award_id":"Young","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6873343303","display_name":null,"funder_award_id":"6170203","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965725760.pdf","grobid_xml":"https://content.openalex.org/works/W2965725760.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W63326460","https://openalex.org/W1557050512","https://openalex.org/W1606766132","https://openalex.org/W1614298861","https://openalex.org/W1973943669","https://openalex.org/W2004353783","https://openalex.org/W2008559906","https://openalex.org/W2019993146","https://openalex.org/W2047332899","https://openalex.org/W2069929199","https://openalex.org/W2154851992","https://openalex.org/W2160957611","https://openalex.org/W2302255633","https://openalex.org/W2553915786","https://openalex.org/W2583466634","https://openalex.org/W2594994417","https://openalex.org/W2786201610","https://openalex.org/W2786780230","https://openalex.org/W2788997482","https://openalex.org/W2808535700","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2897886597","https://openalex.org/W2910952060","https://openalex.org/W2949095675","https://openalex.org/W2950577311","https://openalex.org/W2962834725","https://openalex.org/W2963266340","https://openalex.org/W2963423603","https://openalex.org/W2964098640","https://openalex.org/W4246737633","https://openalex.org/W6688167117","https://openalex.org/W6791858558","https://openalex.org/W6803771590","https://openalex.org/W6863994431","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2091018038","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W2593280956","https://openalex.org/W2004312940","https://openalex.org/W1909961747","https://openalex.org/W1938318326","https://openalex.org/W1969479488"],"abstract_inverted_index":{"Estimating":[0],"the":[1,94],"origin-destination":[2],"travel":[3,118],"time":[4,119],"is":[5],"a":[6,68,75],"fundamental":[7],"problem":[8],"in":[9,98],"many":[10,43],"location-based":[11],"services":[12],"for":[13],"vehicles,":[14],"e.g.,":[15],"ride-hailing,":[16],"vehicle":[17],"dispatching,":[18],"and":[19,64,84,100,116],"route":[20],"planning.":[21],"Recent":[22],"work":[23],"has":[24],"made":[25],"significant":[26],"progress":[27],"to":[28,41,92],"accuracy":[29],"but":[30],"they":[31],"largely":[32],"rely":[33],"on":[34,125],"GPS":[35,59],"traces":[36],"which":[37],"are":[38],"too":[39],"coarse":[40],"model":[42],"personalized":[44],"driving":[45,103],"events.":[46,104],"In":[47],"this":[48],"paper,":[49],"we":[50],"propose":[51],"Customized":[52],"Travel":[53],"Time":[54],"Estimation":[55],"(CTTE)":[56],"that":[57],"fuses":[58],"traces,":[60],"smartphone":[61],"inertial":[62,90],"data,":[63],"road":[65],"network":[66],"within":[67],"deep":[69],"recurrent":[70],"neural":[71],"network.":[72],"It":[73,87],"constructs":[74],"link":[76],"traffic":[77,111,128],"database":[78],"with":[79],"topology":[80],"representation,":[81],"speed":[82,112],"statistics,":[83],"query":[85],"distribution.":[86],"also":[88],"uses":[89],"data":[91],"estimate":[93],"arbitrary":[95],"phone's":[96],"pose":[97],"car,":[99],"detects":[101],"fine-grained":[102],"The":[105],"multi-task":[106],"learning":[107],"structure":[108],"predicts":[109],"both":[110],"at":[113,120],"public":[114],"level":[115],"customized":[117],"personal":[121],"level.":[122],"Extensive":[123],"experiments":[124],"two":[126],"real-world":[127],"datasets":[129],"from":[130],"Didi":[131],"Chuxing":[132],"have":[133],"demonstrated":[134],"our":[135],"effectiveness.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}