{"id":"https://openalex.org/W4309651813","doi":"https://doi.org/10.1145/3557915.3561029","title":"Multi-task graph neural network for truck speed prediction under extreme weather conditions","display_name":"Multi-task graph neural network for truck speed prediction under extreme weather conditions","publication_year":2022,"publication_date":"2022-11-01","ids":{"openalex":"https://openalex.org/W4309651813","doi":"https://doi.org/10.1145/3557915.3561029"},"language":"en","primary_location":{"id":"doi:10.1145/3557915.3561029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3557915.3561029","pdf_url":null,"source":{"id":"https://openalex.org/S4363608995","display_name":"Proceedings of the 30th International Conference on Advances in Geographic Information Systems","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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 30th International Conference on Advances in Geographic Information Systems","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/A5044326803","display_name":"Reza Safarzadeh","orcid":"https://orcid.org/0000-0001-6599-5545"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Reza Safarzadeh Ramhormozi","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016453087","display_name":"Arash Mozhdehi","orcid":"https://orcid.org/0000-0002-9938-3560"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arash Mozhdehi","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001310587","display_name":"Saeid Kalantari","orcid":"https://orcid.org/0000-0002-5882-653X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Saeid Kalantari","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101549955","display_name":"Yunli Wang","orcid":"https://orcid.org/0000-0002-2320-954X"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yunli Wang","raw_affiliation_strings":["National Research Council Canada, Ottawa, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council Canada, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081188742","display_name":"Sun Sun","orcid":"https://orcid.org/0000-0001-5948-3025"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sun Sun","raw_affiliation_strings":["National Research Council Canada, Waterloo, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council Canada, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100327844","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0001-8639-3818"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9955000281333923,"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/T10524","display_name":"Traffic control and management","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/truck","display_name":"Truck","score":0.8455464243888855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7150026559829712},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5888992547988892},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5630629062652588},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5598400235176086},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5089504718780518},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4674231708049774},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4404986500740051},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4226936101913452},{"id":"https://openalex.org/keywords/traffic-speed","display_name":"Traffic speed","score":0.42093729972839355},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3985641598701477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3964437246322632},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3208412528038025},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.21219441294670105},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18594631552696228},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.1552797555923462}],"concepts":[{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.8455464243888855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7150026559829712},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5888992547988892},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5630629062652588},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5598400235176086},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5089504718780518},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4674231708049774},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4404986500740051},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4226936101913452},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.42093729972839355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3985641598701477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3964437246322632},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3208412528038025},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.21219441294670105},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18594631552696228},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.1552797555923462},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3557915.3561029","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3557915.3561029","pdf_url":null,"source":{"id":"https://openalex.org/S4363608995","display_name":"Proceedings of the 30th International Conference on Advances in Geographic Information Systems","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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 30th International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:cisti-icist.nrc-cnrc.ca:cistinparc:dfcbac75-13f5-4d40-8683-e7acc6d92e20","is_oa":false,"landing_page_url":"https://nrc-publications.canada.ca/eng/view/object/?id=dfcbac75-13f5-4d40-8683-e7acc6d92e20","pdf_url":null,"source":{"id":"https://openalex.org/S7407055245","display_name":"NPARC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4300000071525574,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1984006069","https://openalex.org/W2008483594","https://openalex.org/W2041567331","https://openalex.org/W2082454007","https://openalex.org/W2125817951","https://openalex.org/W2135822894","https://openalex.org/W2137541292","https://openalex.org/W2148143831","https://openalex.org/W2163089819","https://openalex.org/W2372844811","https://openalex.org/W2572939427","https://openalex.org/W2573587735","https://openalex.org/W2901504064","https://openalex.org/W2907874667","https://openalex.org/W2942392852","https://openalex.org/W2991205212","https://openalex.org/W3021728802","https://openalex.org/W3100805595","https://openalex.org/W3133663379","https://openalex.org/W3152893301","https://openalex.org/W3164522113"],"related_works":["https://openalex.org/W1517019597","https://openalex.org/W1968776045","https://openalex.org/W2296713838","https://openalex.org/W767149399","https://openalex.org/W3036261569","https://openalex.org/W2889950528","https://openalex.org/W575062473","https://openalex.org/W4297672583","https://openalex.org/W2357934771","https://openalex.org/W3033024819"],"abstract_inverted_index":{"Truck":[0,173],"speed":[1,66,78,145,225],"prediction":[2,31,67,79,228,269],"plays":[3],"a":[4,14,129,201,238],"key":[5],"role":[6],"in":[7,98,116,188],"truck":[8,20,43,54,65,77,86,92,122,144,190,224],"transportation":[9],"management.":[10],"However,":[11],"it":[12],"is":[13,57,180,243],"very":[15],"challenging":[16,71],"task":[17],"since":[18],"the":[19,28,34,38,64,76,91,117,121,143,161,166,170,184,189,208,220,223,235,247,250,261],"traffic":[21,30,44,55,87,101,174,191,268],"usually":[22],"shows":[23],"complex":[24],"patterns.":[25],"Most":[26],"of":[27,42,83,120,169,203,211],"existing":[29],"methods":[32],"lack":[33],"ability":[35],"to":[36,74,141,182,206,245],"model":[37],"dynamic":[39],"spatial-temporal":[40],"correlations":[41],"or":[45],"ignore":[46],"contributing":[47],"contextual":[48,194,212],"factors":[49],"that":[50,260],"impact":[51],"traffic.":[52],"Also,":[53],"data":[56,239,248],"typically":[58],"sparse":[59],"and":[60,103,193,226],"noisy,":[61],"which":[62],"makes":[63],"an":[68],"even":[69],"more":[70],"task.":[72],"How":[73],"improve":[75],"by":[80,108,165],"taking":[81],"advantage":[82],"other":[84],"relevant":[85],"information":[88,221],"(such":[89],"as":[90],"flow)":[93],"has":[94],"not":[95],"been":[96],"investigated":[97],"depth.":[99],"Additionally,":[100],"congestions":[102],"poor":[104],"driving":[105],"conditions":[106,111],"caused":[107],"extreme":[109,147,251],"weather":[110,148,252],"can":[112],"make":[113],"sudden":[114],"changes":[115],"general":[118],"pattern":[119],"speed.":[123],"In":[124],"this":[125],"paper,":[126],"we":[127],"propose":[128],"novel":[130],"Multi-Task":[131],"Context":[132],"Based":[133],"Gated":[134],"Recurrent":[135],"Unit":[136],"Graph":[137],"Convolutional":[138],"Network":[139],"(MT-C2G)":[140],"predict":[142],"under":[146],"conditions.":[149,253],"MT-C2G":[150,263],"includes":[151],"four":[152],"major":[153],"components:":[154],"The":[155,214],"spatial":[156,162],"dependence":[157,177,197],"learning":[158,216],"component":[159,179,199,217],"captures":[160],"dependencies":[163,186,210],"shaped":[164],"topological":[167],"structure":[168],"road":[171],"network.":[172],"feature":[175,195],"temporal":[176,185,196,209],"modeling":[178,198],"built":[181],"acquire":[183],"involved":[187],"features,":[192],"employs":[200],"layer":[202],"GRU":[204],"units":[205],"capture":[207],"factors.":[213],"multi-task":[215],"then":[218],"leverages":[219],"between":[222],"flow":[227],"tasks":[229],"through":[230],"attention":[231],"mechanism":[232],"for":[233],"improving":[234],"performance.":[236],"Moreover,":[237],"augmentation":[240],"method":[241],"SMOTE":[242],"utilized":[244],"balance":[246],"with":[249],"Experiments":[254],"on":[255],"two":[256],"real":[257],"datasets":[258],"demonstrate":[259],"proposed":[262],"fairly":[264],"outperforms":[265],"six":[266],"state-of-the-art":[267],"methods.":[270]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}