{"id":"https://openalex.org/W2809128166","doi":"https://doi.org/10.1145/3219819.3219900","title":"Learning to Estimate the Travel Time","display_name":"Learning to Estimate the Travel Time","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2809128166","doi":"https://doi.org/10.1145/3219819.3219900","mag":"2809128166"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3219900","doi":"https://doi.org/10.1145/3219819.3219900","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3219900","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100401173","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0001-5946-8407","observed_orcids":["https://orcid.org/0000-0001-5946-8407","https://orcid.org/0000-0003-4873-505X","https://orcid.org/0009-0005-3278-8255","https://orcid.org/0000-0002-0435-1057","https://orcid.org/0000-0002-5482-6524","https://orcid.org/0000-0002-6150-3846","https://orcid.org/0000-0002-9318-0084","https://orcid.org/0000-0003-4802-8435","https://orcid.org/0000-0003-4959-3372","https://orcid.org/0009-0000-8832-3178","https://orcid.org/0009-0002-0063-134X","https://orcid.org/0009-0004-8177-0038"]},"institutions":[{"id":"https://openalex.org/I4401726870","display_name":"Didi Chuxing (China)","ror":"https://ror.org/02ksqcf75","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726870"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["DiDi AI Labs &amp;Didi Chuxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs &amp;Didi Chuxing, Beijing, China","institution_ids":["https://openalex.org/I4401726870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112365916","display_name":"Kun Fu","orcid":"https://orcid.org/0000-0002-0450-6469","observed_orcids":["https://orcid.org/0000-0002-0450-6469","https://orcid.org/0000-0002-8647-9627","https://orcid.org/0000-0002-0397-3110"]},"institutions":[{"id":"https://openalex.org/I4401726870","display_name":"Didi Chuxing (China)","ror":"https://ror.org/02ksqcf75","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726870"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Fu","raw_affiliation_strings":["DiDi AI Labs &amp;Didi Chuxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs &amp;Didi Chuxing, Beijing, China","institution_ids":["https://openalex.org/I4401726870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010419481","display_name":"Jieping Ye","orcid":"https://orcid.org/0000-0001-8662-5818","observed_orcids":["https://orcid.org/0000-0001-8662-5818"]},"institutions":[{"id":"https://openalex.org/I4401726870","display_name":"Didi Chuxing (China)","ror":"https://ror.org/02ksqcf75","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726870"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jieping Ye","raw_affiliation_strings":["DiDi AI Labs &amp;Didi Chuxing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DiDi AI Labs &amp;Didi Chuxing, Beijing, China","institution_ids":["https://openalex.org/I4401726870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4401726870"],"apc_list":null,"apc_paid":null,"fwci":31.5958,"has_fulltext":false,"cited_by_count":269,"citation_normalized_percentile":{"value":0.99693396,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"858","last_page":"866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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":0.00019999999494757503,"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/T11106","display_name":"Data Management and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/travel-time-estimation","display_name":"travel time estimation","score":0.8999999761581421},{"id":"https://openalex.org/keywords/estimated-time-of-arrival","display_name":"estimated time of arrival","score":0.9950000047683716},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"floating car data","score":0.6790000200271606},{"id":"https://openalex.org/keywords/location-based-services","display_name":"location-based services","score":0.8489999771118164},{"id":"https://openalex.org/keywords/vehicle-navigation","display_name":"vehicle navigation","score":0.492000013589859},{"id":"https://openalex.org/keywords/intelligent-transportation-systems","display_name":"intelligent transportation systems","score":0.7160000205039978},{"id":"https://openalex.org/keywords/didi-chuxing","display_name":"Didi Chuxing","score":0.7749999761581421}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77747642993927},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.620759904384613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5964113473892212},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5608420968055725},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5360878109931946},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5022621154785156},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5002312660217285},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.4883365333080292},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.44236665964126587},{"id":"https://openalex.org/C116537","wikidata":"https://www.wikidata.org/wiki/Q2169973","display_name":"Service provider","level":3,"score":0.4382319748401642},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.12960472702980042},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12694066762924194},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219819.3219900","doi":"https://doi.org/10.1145/3219819.3219900","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3219900","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"},{"display_name":"Sustainable cities and communities","score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1557050512","https://openalex.org/W1678356000","https://openalex.org/W1788809966","https://openalex.org/W1977594918","https://openalex.org/W2064675550","https://openalex.org/W2069929199","https://openalex.org/W2084143084","https://openalex.org/W2144475703","https://openalex.org/W2146502635","https://openalex.org/W2163605009","https://openalex.org/W2168791259","https://openalex.org/W2295418620","https://openalex.org/W2295598076","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2550072831","https://openalex.org/W2550311513","https://openalex.org/W2564701384","https://openalex.org/W2597851033","https://openalex.org/W2962917186","https://openalex.org/W2964098640","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W3102115934","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W3112483519","https://openalex.org/W4388763833","https://openalex.org/W4407074822","https://openalex.org/W4308081127","https://openalex.org/W4285282135","https://openalex.org/W4383899492","https://openalex.org/W4365518742","https://openalex.org/W3084879683","https://openalex.org/W3214360274","https://openalex.org/W2788997482"],"abstract_inverted_index":{"Vehicle":[0],"travel":[1,52,101,145],"time":[2,6,53,102],"estimation":[3],"or":[4],"estimated":[5],"of":[7,12,73,131,142,160,166],"arrival":[8],"(ETA)":[9],"is":[10,20],"one":[11],"the":[13,50,86,100,150,182,189],"most":[14],"important":[15,23],"location-based":[16],"services":[17,158],"(LBS).":[18],"It":[19],"becoming":[21],"increasingly":[22],"and":[24,36,122,163],"has":[25],"been":[26],"widely":[27],"used":[28],"as":[29,62,186,188],"a":[30,43,63,70,92,104,108],"basic":[31],"service":[32],"in":[33],"navigation":[34],"systems":[35],"intelligent":[37],"transportation":[38],"systems.":[39],"This":[40],"paper":[41],"presents":[42],"novel":[44],"machine":[45,81],"learning":[46,82,95,178,184],"solution":[47,138,152],"to":[48,84,97,127],"predict":[49,99],"vehicle":[51,144],"based":[54,68],"on":[55,69,153],"floating-car":[56],"data.":[57,146],"First,":[58],"we":[59,77,90],"formulate":[60],"ETA":[61,161],"pure":[64],"spatial-temporal":[65],"regression":[66,87],"problem":[67],"large":[71],"set":[72],"effective":[74],"features.":[75],"Second,":[76],"adapt":[78],"different":[79],"existing":[80],"models":[83],"solve":[85],"problem.":[88],"Furthermore,":[89],"propose":[91],"Wide-Deep-Recurrent":[93],"(WDR)":[94],"model":[96],"accurately":[98],"along":[103],"given":[105,109],"route":[106],"at":[107],"departure":[110],"time.":[111],"We":[112,135,147],"then":[113],"jointly":[114],"train":[115],"wide":[116],"linear":[117],"models,":[118],"deep":[119,177],"neural":[120,124],"networks":[121,125],"recurrent":[123],"together":[126],"take":[128],"full":[129],"advantages":[130],"all":[132],"three":[133],"models.":[134],"evaluate":[136],"our":[137,175],"offline":[139],"with":[140],"millions":[141,165],"historical":[143],"also":[148],"deploy":[149],"proposed":[151,176],"Didi":[154],"Chuxing's":[155],"platform,":[156],"which":[157],"billions":[159],"requests":[162],"benefits":[164],"customers":[167],"per":[168],"day.":[169],"Our":[170],"extensive":[171],"evaluations":[172],"show":[173],"that":[174],"algorithm":[179],"significantly":[180],"outperforms":[181],"state-of-the-art":[183],"algorithms,":[185],"well":[187],"solutions":[190],"provided":[191],"by":[192],"leading":[193],"industry":[194],"LBS":[195],"providers.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":52},{"year":2022,"cited_by_count":43},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":35},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-06-29T00:00:00"}
