{"id":"https://openalex.org/W4382322862","doi":"https://doi.org/10.1145/3580305.3599767","title":"A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation","display_name":"A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4382322862","doi":"https://doi.org/10.1145/3580305.3599767"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599767","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2306.14421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103057074","display_name":"Siqi Lai","orcid":"https://orcid.org/0000-0003-3564-1783"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Siqi Lai","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457935","display_name":"Weijia Zhang","orcid":"https://orcid.org/0000-0001-5085-5216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weijia Zhang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100458897","display_name":"Hao Liu","orcid":"https://orcid.org/0000-0003-4271-1567"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Liu","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103057074"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5782,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.84356235,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4346","last_page":"4356"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","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"}},"topics":[{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","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/T12095","display_name":"Vehicle emissions and performance","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9969000220298767,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7846698760986328},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.7157865166664124},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6176763772964478},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5542383790016174},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.5339941382408142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5037321448326111},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.4952913820743561},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.4611983001232147},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.45522722601890564},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42801111936569214},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12690359354019165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846698760986328},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.7157865166664124},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6176763772964478},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5542383790016174},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.5339941382408142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5037321448326111},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.4952913820743561},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.4611983001232147},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.45522722601890564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42801111936569214},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12690359354019165},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3580305.3599767","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2306.14421","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.14421","pdf_url":"https://arxiv.org/pdf/2306.14421","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-130673","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-130673","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"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":"Conference paper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2306.14421","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.14421","pdf_url":"https://arxiv.org/pdf/2306.14421","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2005150936","display_name":null,"funder_award_id":"62102110","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2143388661","https://openalex.org/W2169805129","https://openalex.org/W2387506654","https://openalex.org/W2768256553","https://openalex.org/W2790707130","https://openalex.org/W2808766325","https://openalex.org/W2809128166","https://openalex.org/W2950883684","https://openalex.org/W2965312081","https://openalex.org/W2966632286","https://openalex.org/W2999881469","https://openalex.org/W3081469395","https://openalex.org/W3097982226","https://openalex.org/W3132328326","https://openalex.org/W3134343811","https://openalex.org/W3155157989","https://openalex.org/W3171560357","https://openalex.org/W3181487872","https://openalex.org/W3194849262","https://openalex.org/W4205587414","https://openalex.org/W4210611985","https://openalex.org/W4212816057","https://openalex.org/W4285451014","https://openalex.org/W4290877497","https://openalex.org/W4290943551","https://openalex.org/W4290943973","https://openalex.org/W4292122507","https://openalex.org/W6600018615"],"related_works":["https://openalex.org/W2807758032","https://openalex.org/W2152103536","https://openalex.org/W4224254130","https://openalex.org/W3048948123","https://openalex.org/W413879896","https://openalex.org/W1983530038","https://openalex.org/W2972374246","https://openalex.org/W3205006318","https://openalex.org/W4293174494","https://openalex.org/W2062628630"],"abstract_inverted_index":{"Vehicle":[0],"Energy":[1],"Consumption":[2],"(VEC)":[3],"estimation":[4],"aims":[5],"to":[6,24,42,58,93,119,145],"predict":[7],"the":[8,44,48,59,95,106,164],"total":[9],"energy":[10,49,81],"required":[11],"for":[12,78,135],"a":[13,73,88,113,125,139],"given":[14,126],"trip":[15,25],"before":[16],"it":[17],"starts,":[18],"which":[19,128],"is":[20,143,181],"of":[21,51,108,166],"great":[22],"importance":[23],"planning":[26],"and":[27,132,152,173],"transportation":[28],"sustainability.":[29],"Existing":[30],"approaches":[31],"mainly":[32],"focus":[33],"on":[34,105,124,159],"extracting":[35],"statistically":[36],"significant":[37],"factors":[38],"from":[39],"typical":[40],"trips":[41],"improve":[43],"VEC":[45,136],"estimation.":[46,83,137],"However,":[47],"consumption":[50,82],"each":[52],"vehicle":[53,80],"may":[54],"diverge":[55],"widely":[56],"due":[57],"personalized":[60,79],"driving":[61,115,122],"behavior":[62,90,116],"under":[63],"varying":[64],"travel":[65],"contexts.":[66],"To":[67],"this":[68,70],"end,":[69],"paper":[71],"proposes":[72],"preference-aware":[74],"meta-optimization":[75,141],"framework":[76,169],"Meta-Pec":[77],"Specifically,":[84],"we":[85,111],"first":[86],"propose":[87],"spatiotemporal":[89],"learning":[91,151,176],"module":[92,118],"capture":[94],"latent":[96],"driver":[97,109],"preference":[98],"hidden":[99],"in":[100],"historical":[101],"trips.":[102],"Moreover,":[103],"based":[104],"memorization":[107],"preference,":[110],"devise":[112],"selection-based":[114],"prediction":[117],"infer":[120],"driver-specific":[121,140],"patterns":[123],"route,":[127],"provides":[129],"additional":[130],"basis":[131],"supervision":[133],"signals":[134],"Besides,":[138],"scheme":[142],"proposed":[144,168],"enable":[146],"fast":[147],"model":[148],"adaption":[149],"by":[150],"sharing":[153],"transferable":[154],"knowledge":[155],"globally.":[156],"Extensive":[157],"experiments":[158],"two":[160],"real-world":[161],"datasets":[162],"show":[163],"superiority":[165],"our":[167],"against":[170],"ten":[171],"numerical":[172],"data-driven":[174],"machine":[175],"baselines.":[177],"The":[178],"source":[179],"code":[180],"available":[182],"at":[183],"https://github.com/usail-hkust/Meta-Pec.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
