{"id":"https://openalex.org/W2153458569","doi":"https://doi.org/10.1145/2623330.2623653","title":"Inferring gas consumption and pollution emission of vehicles throughout a city","display_name":"Inferring gas consumption and pollution emission of vehicles throughout a city","publication_year":2014,"publication_date":"2014-08-22","ids":{"openalex":"https://openalex.org/W2153458569","doi":"https://doi.org/10.1145/2623330.2623653","mag":"2153458569"},"language":"en","primary_location":{"id":"doi:10.1145/2623330.2623653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5039500313","display_name":"Jingbo Shang","orcid":"https://orcid.org/0000-0002-7249-4404"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingbo Shang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681023","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0002-5224-4344"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064143299","display_name":"Wenzhu Tong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhu Tong","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087298985","display_name":"Eric Chang","orcid":"https://orcid.org/0000-0002-9678-5994"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Eric Chang","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001571390","display_name":"Yong Yu","orcid":"https://orcid.org/0000-0003-0281-8271"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Yu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":43.3741,"has_fulltext":false,"cited_by_count":252,"citation_normalized_percentile":{"value":0.99919355,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1027","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9986000061035156,"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":0.9984999895095825,"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/taxis","display_name":"Taxis","score":0.718416154384613},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6921345591545105},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6557931900024414},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.6496936678886414},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5247114896774292},{"id":"https://openalex.org/keywords/fuel-efficiency","display_name":"Fuel efficiency","score":0.46209731698036194},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4103092551231384},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4051131308078766},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.38269710540771484},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37222951650619507},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34404516220092773},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.17842066287994385},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.16241100430488586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16002756357192993},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12373882532119751},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12367114424705505}],"concepts":[{"id":"https://openalex.org/C183373512","wikidata":"https://www.wikidata.org/wiki/Q949618","display_name":"Taxis","level":2,"score":0.718416154384613},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6921345591545105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6557931900024414},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6496936678886414},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5247114896774292},{"id":"https://openalex.org/C45882903","wikidata":"https://www.wikidata.org/wiki/Q5042317","display_name":"Fuel efficiency","level":2,"score":0.46209731698036194},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4103092551231384},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4051131308078766},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.38269710540771484},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37222951650619507},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34404516220092773},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17842066287994385},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.16241100430488586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16002756357192993},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12373882532119751},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12367114424705505},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2623330.2623653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.713.9988","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.713.9988","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.buffalo.edu/%7Elusu/cse726/papers/Inferring%20Gas%20Consumption%20and%20Pollution%20Emission%20of%20Vehicles%20throughout%20a%20City.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W13700871","https://openalex.org/W16548326","https://openalex.org/W83801381","https://openalex.org/W1514578609","https://openalex.org/W1827384124","https://openalex.org/W2031674781","https://openalex.org/W2045487859","https://openalex.org/W2045492180","https://openalex.org/W2049176600","https://openalex.org/W2051025556","https://openalex.org/W2070505525","https://openalex.org/W2076977777","https://openalex.org/W2112738128","https://openalex.org/W2132748629","https://openalex.org/W2146616964","https://openalex.org/W2166771065","https://openalex.org/W2275448180","https://openalex.org/W2379670165","https://openalex.org/W2917955659","https://openalex.org/W2999946671","https://openalex.org/W3148998644"],"related_works":["https://openalex.org/W2731640799","https://openalex.org/W3145095895","https://openalex.org/W2594548639","https://openalex.org/W4387544810","https://openalex.org/W2978498151","https://openalex.org/W2782837293","https://openalex.org/W1946755446","https://openalex.org/W2388377527","https://openalex.org/W565532978","https://openalex.org/W2114323843"],"abstract_inverted_index":{"This":[0],"paper":[1],"instantly":[2],"infers":[3],"the":[4,59,70,77,86,94,143,158,191,240,248,258],"gas":[5,50,201],"consumption":[6,202],"and":[7,67,137,184,194,203,253],"pollution":[8,65,75],"emission":[9],"of":[10,29,58,73,89,160,187,197,234,242,250,260],"vehicles":[11,30,62,161],"traveling":[12],"on":[13,120,209,218],"a":[14,19,27,113,121,150,188,198,232],"city's":[15],"road":[16,47,91,101,164,199],"network":[17],"in":[18,76,229],"current":[20],"time":[21],"slot,":[22],"using":[23,93,221],"GPS":[24,95,222],"trajectories":[25,96,107,223],"from":[26,61,130],"sample":[28],"(e.g.,":[31],"taxicabs).":[32],"The":[33,55,237],"knowledge":[34],"can":[35,63,205],"be":[36,206],"used":[37],"to":[38,140,156],"suggest":[39],"cost-efficient":[40],"driving":[41],"routes":[42],"as":[43,45,179],"well":[44],"identifying":[46],"segments":[48,102],"where":[49],"has":[51],"been":[52],"wasted":[53],"significantly.":[54],"instant":[56],"estimation":[57],"emissions":[60,204],"enable":[64],"alerts":[66],"help":[68],"diagnose":[69],"root":[71],"cause":[72],"air":[74],"long":[78],"run.":[79],"In":[80],"our":[81,215,243],"method,":[82],"we":[83,111],"first":[84],"compute":[85],"travel":[87,180,192],"speed":[88,193],"each":[90,163],"segment":[92,165],"received":[97],"recently.":[98],"As":[99],"many":[100],"are":[103],"not":[104],"traversed":[105],"by":[106,225],"(i.e.,":[108],"data":[109,132,136,144],"sparsity),":[110],"propose":[112,149],"Travel":[114],"Speed":[115],"Estimation":[116],"(TSE)":[117],"model":[118,155],"based":[119,208,217],"context-aware":[122],"matrix":[123],"factorization":[124],"approach.":[125],"TSE":[126],"leverages":[127],"features":[128,186],"learned":[129],"other":[131],"sources,":[133],"e.g.,":[134],"map":[135],"historical":[138],"trajectories,":[139],"deal":[141],"with":[142],"sparsity":[145],"problem.":[146],"We":[147,213],"then":[148],"Traffic":[151],"Volume":[152],"Inference":[153],"(TVI)":[154],"infer":[157],"number":[159],"passing":[162],"per":[166],"minute.":[167],"TVI":[168],"is":[169],"an":[170],"unsupervised":[171],"Bayesian":[172],"Network":[173],"that":[174],"incorporates":[175],"multiple":[176],"factors,":[177],"such":[178],"speed,":[181],"weather":[182],"conditions":[183],"geographical":[185],"road.":[189],"Given":[190],"traffic":[195],"volume":[196],"segment,":[200],"calculated":[207],"existing":[210],"environmental":[211],"theories.":[212],"evaluate":[214],"method":[216,244],"extensive":[219],"experiments":[220],"generated":[224],"over":[226,231,245],"32,000":[227],"taxis":[228],"Beijing":[230],"period":[233],"two":[235],"months.":[236],"results":[238],"demonstrate":[239],"advantages":[241],"baselines,":[246],"validating":[247],"contribution":[249],"its":[251],"components":[252],"finding":[254],"interesting":[255],"discoveries":[256],"for":[257],"benefit":[259],"society.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":28},{"year":2019,"cited_by_count":36},{"year":2018,"cited_by_count":31},{"year":2017,"cited_by_count":28},{"year":2016,"cited_by_count":34},{"year":2015,"cited_by_count":25},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}