{"id":"https://openalex.org/W4403473705","doi":"https://doi.org/10.1007/s00521-024-10461-2","title":"Implicit sensing self-supervised learning based on graph multi-pretext tasks for traffic flow prediction","display_name":"Implicit sensing self-supervised learning based on graph multi-pretext tasks for traffic flow prediction","publication_year":2024,"publication_date":"2024-10-16","ids":{"openalex":"https://openalex.org/W4403473705","doi":"https://doi.org/10.1007/s00521-024-10461-2"},"language":"en","primary_location":{"id":"doi:10.1007/s00521-024-10461-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-024-10461-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-024-10461-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00521-024-10461-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009370519","display_name":"Ali Reza Sattarzadeh","orcid":"https://orcid.org/0000-0003-0431-6654"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Ali Reza Sattarzadeh","raw_affiliation_strings":["School of Engineering, Deakin University, Waurn Ponds, Geelong, VIC, 3216, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0431-6654","affiliations":[{"raw_affiliation_string":"School of Engineering, Deakin University, Waurn Ponds, Geelong, VIC, 3216, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037113249","display_name":"Pubudu N. Pathirana","orcid":"https://orcid.org/0000-0001-8014-7798"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Pubudu Nishantha Pathirana","raw_affiliation_strings":["School of Engineering, Deakin University, Waurn Ponds, Geelong, VIC, 3216, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Deakin University, Waurn Ponds, Geelong, VIC, 3216, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080554686","display_name":"Marimuthu Palaniswami","orcid":"https://orcid.org/0000-0002-3635-4252"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Marimuthu Palaniswami","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009370519"],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.0648,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75347289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"37","issue":"2","first_page":"1041","last_page":"1066"},"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":0.995199978351593,"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/computer-science","display_name":"Computer science","score":0.7636598944664001},{"id":"https://openalex.org/keywords/pretext","display_name":"Pretext","score":0.7577391862869263},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.7186418175697327},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5633683204650879},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5229057669639587},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48952019214630127},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4150979518890381},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2874319553375244},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.131851464509964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7636598944664001},{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.7577391862869263},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.7186418175697327},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5633683204650879},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5229057669639587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48952019214630127},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4150979518890381},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2874319553375244},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.131851464509964},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00521-024-10461-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-024-10461-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-024-10461-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00521-024-10461-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-024-10461-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-024-10461-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320970","display_name":"Deakin University","ror":"https://ror.org/02czsnj07"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403473705.pdf","grobid_xml":"https://content.openalex.org/works/W4403473705.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W1596717185","https://openalex.org/W1973943669","https://openalex.org/W1991770012","https://openalex.org/W2002644193","https://openalex.org/W2012051283","https://openalex.org/W2040297119","https://openalex.org/W2083238230","https://openalex.org/W2090192376","https://openalex.org/W2125817951","https://openalex.org/W2145039203","https://openalex.org/W2150010190","https://openalex.org/W2158107760","https://openalex.org/W2165991108","https://openalex.org/W2171234954","https://openalex.org/W2190353863","https://openalex.org/W2579495707","https://openalex.org/W2613331518","https://openalex.org/W2756203131","https://openalex.org/W2793820729","https://openalex.org/W2883725317","https://openalex.org/W2884738862","https://openalex.org/W2901504064","https://openalex.org/W2904832339","https://openalex.org/W2905967367","https://openalex.org/W2907492528","https://openalex.org/W2935726879","https://openalex.org/W2947666164","https://openalex.org/W2950817888","https://openalex.org/W2963091558","https://openalex.org/W2963677766","https://openalex.org/W2963791934","https://openalex.org/W2965341826","https://openalex.org/W2972752351","https://openalex.org/W2996847713","https://openalex.org/W2997128522","https://openalex.org/W2999301586","https://openalex.org/W3008849576","https://openalex.org/W3011638589","https://openalex.org/W3015851013","https://openalex.org/W3026400623","https://openalex.org/W3027664001","https://openalex.org/W3035524453","https://openalex.org/W3043674345","https://openalex.org/W3082034060","https://openalex.org/W3103720336","https://openalex.org/W3122939190","https://openalex.org/W3126693873","https://openalex.org/W3128987237","https://openalex.org/W3133663379","https://openalex.org/W3137575552","https://openalex.org/W3170140111","https://openalex.org/W3171007011","https://openalex.org/W3174022889","https://openalex.org/W3176940011","https://openalex.org/W4319597002","https://openalex.org/W4321109783","https://openalex.org/W4323849306","https://openalex.org/W4382236906","https://openalex.org/W4385287316","https://openalex.org/W4387697025","https://openalex.org/W4388230580","https://openalex.org/W4388681972","https://openalex.org/W4389090514","https://openalex.org/W4389092500","https://openalex.org/W4389392210","https://openalex.org/W4389484179","https://openalex.org/W4393318754","https://openalex.org/W6600175266","https://openalex.org/W6610114515","https://openalex.org/W6636026467","https://openalex.org/W6679942958","https://openalex.org/W6811416509"],"related_works":["https://openalex.org/W161456234","https://openalex.org/W3123043866","https://openalex.org/W2765162471","https://openalex.org/W2367130511","https://openalex.org/W4235007455","https://openalex.org/W2354300066","https://openalex.org/W2996988663","https://openalex.org/W2276802262","https://openalex.org/W2089741817","https://openalex.org/W2358993285"],"abstract_inverted_index":{"Abstract":[0],"In":[1,192],"recent":[2],"years,":[3],"spatio-temporal":[4,20],"graph":[5],"neural":[6],"networks":[7],"(GNNs)":[8],"have":[9,159,170],"successfully":[10],"been":[11],"used":[12],"to":[13,44],"improve":[14],"traffic":[15,24,36,65,101,140,156],"prediction":[16,157],"by":[17],"modeling":[18],"intricate":[19],"dependencies":[21],"in":[22,119,125,138,174],"irregular":[23],"networks.":[25],"However,":[26],"these":[27],"approaches":[28],"may":[29],"not":[30],"capture":[31],"the":[32,51,115,161,164,172,177,197,200],"intrinsic":[33],"properties":[34],"of":[35,100,117,163,182],"data":[37,70],"and":[38,81,90,105,122,128,150,179,184,188,207],"can":[39],"suffer":[40],"from":[41],"overfitting":[42],"due":[43],"their":[45],"local":[46,148],"nature.":[47],"This":[48,94],"paper":[49],"introduces":[50],"Implicit":[52],"Sensing":[53],"Self-Supervised":[54],"learning":[55,89,120,168,194],"model":[56],"(ISSS),":[57],"which":[58],"leverages":[59],"a":[60,97],"multi-pretext":[61],"task":[62],"framework":[63],"for":[64,176,186,196,204,209],"flow":[66,141],"prediction.":[67],"By":[68],"transforming":[69],"into":[71],"an":[72],"alternative":[73],"feature":[74],"space,":[75],"ISSS":[76,118,131],"effectively":[77],"captures":[78],"both":[79,126],"specific":[80],"general":[82,121],"representations":[83],"through":[84],"self-supervised":[85],"tasks,":[86,190],"including":[87],"contrastive":[88],"spatial":[91],"jigsaw":[92],"puzzles.":[93],"enhancement":[95],"promotes":[96],"deeper":[98],"understanding":[99],"features,":[102],"improved":[103],"regularization,":[104],"more":[106],"accurate":[107],"representations.":[108],"Comparative":[109],"experiments":[110],"on":[111],"six":[112],"datasets":[113,181],"demonstrate":[114],"effectiveness":[116],"discriminative":[123],"features":[124],"supervised":[127,193],"unsupervised":[129],"modes.":[130],"outperforms":[132],"existing":[133],"models,":[134],"demonstrating":[135],"its":[136],"capabilities":[137],"improving":[139],"predictions":[142],"while":[143],"addressing":[144],"challenges":[145],"associated":[146],"with":[147],"operations":[149],"overfitting.":[151],"Comprehensive":[152],"evaluations":[153],"across":[154],"various":[155],"datasets,":[158,199],"established":[160],"validity":[162],"proposed":[165],"approach.":[166],"Unsupervised":[167],"scenarios":[169],"shown":[171],"improvements":[173,201],"RMSE":[175],"METR-LA":[178],"PEMSBAY":[180],"0.39":[183],"0.35":[185],"location-dependent":[187,205],"location-independent":[189,210],"respectively.":[191],"scenarios,":[195],"same":[198],"were":[202],"1.16":[203],"tasks":[206],"0.55":[208],"tasks.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}