{"id":"https://openalex.org/W4205627639","doi":"https://doi.org/10.1109/bigdata52589.2021.9671591","title":"OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification","display_name":"OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205627639","doi":"https://doi.org/10.1109/bigdata52589.2021.9671591"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671591","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5036086282","display_name":"Hashim Abu-gellban","orcid":"https://orcid.org/0000-0003-4498-8543"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hashim Abu-Gellban","raw_affiliation_strings":["Department of Computer Science, Texas Tech University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006229285","display_name":"Yu Zhuang","orcid":"https://orcid.org/0000-0001-5561-1480"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Zhuang","raw_affiliation_strings":["Department of Computer Science, Texas Tech University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102021622","display_name":"Long Nguyen","orcid":"https://orcid.org/0000-0002-9507-8198"},"institutions":[{"id":"https://openalex.org/I118073183","display_name":"Meharry Medical College","ror":"https://ror.org/00k63dq23","country_code":"US","type":"education","lineage":["https://openalex.org/I118073183"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Nguyen","raw_affiliation_strings":["Computer Science and Data Science, Meharry Medical College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Data Science, Meharry Medical College","institution_ids":["https://openalex.org/I118073183"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101706801","display_name":"Fang Jin","orcid":"https://orcid.org/0000-0002-6606-5232"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fang Jin","raw_affiliation_strings":["Department of Statistics, George Washington University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Washington University","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043680593","display_name":"Zhenkai Zhang","orcid":"https://orcid.org/0000-0003-2439-0923"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenkai Zhang","raw_affiliation_strings":["Computer Science Division, Clemson University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Division, Clemson University","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4915,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62079832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"29","issue":null,"first_page":"2857","last_page":"2866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.7418081164360046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5819206237792969},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5198372006416321},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.47615620493888855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4731650948524475},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45759233832359314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35293474793434143},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3506196141242981}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7418081164360046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5819206237792969},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5198372006416321},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47615620493888855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4731650948524475},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45759233832359314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35293474793434143},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3506196141242981},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671591","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W1597227227","https://openalex.org/W1903029394","https://openalex.org/W1924770834","https://openalex.org/W1987166016","https://openalex.org/W2033396082","https://openalex.org/W2096045964","https://openalex.org/W2157331557","https://openalex.org/W2203940127","https://openalex.org/W2286343943","https://openalex.org/W2295598076","https://openalex.org/W2309832917","https://openalex.org/W2338065342","https://openalex.org/W2353267094","https://openalex.org/W2546571074","https://openalex.org/W2551393996","https://openalex.org/W2581867724","https://openalex.org/W2605244415","https://openalex.org/W2605751614","https://openalex.org/W2606851531","https://openalex.org/W2734718015","https://openalex.org/W2753782371","https://openalex.org/W2754051771","https://openalex.org/W2783323081","https://openalex.org/W2792440251","https://openalex.org/W2793199760","https://openalex.org/W2799462250","https://openalex.org/W2803275646","https://openalex.org/W2805557887","https://openalex.org/W2897801235","https://openalex.org/W2897940545","https://openalex.org/W2901710176","https://openalex.org/W2901946743","https://openalex.org/W2903779325","https://openalex.org/W2915329514","https://openalex.org/W2933725428","https://openalex.org/W2949364977","https://openalex.org/W2957552952","https://openalex.org/W2963420686","https://openalex.org/W2963535483","https://openalex.org/W2968846613","https://openalex.org/W2991401306","https://openalex.org/W3012919764","https://openalex.org/W3033729212","https://openalex.org/W3037593814","https://openalex.org/W3039418901","https://openalex.org/W3045156416","https://openalex.org/W3094568574","https://openalex.org/W3095854630","https://openalex.org/W3111016868","https://openalex.org/W3112094888","https://openalex.org/W3121092655","https://openalex.org/W3136600992","https://openalex.org/W3174367756","https://openalex.org/W3209003283","https://openalex.org/W4285328045","https://openalex.org/W4288106384","https://openalex.org/W6635737673","https://openalex.org/W6640212811","https://openalex.org/W6743731764","https://openalex.org/W6744506962","https://openalex.org/W6766647898","https://openalex.org/W6768251945","https://openalex.org/W6779346198","https://openalex.org/W6780809728"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W2043093291","https://openalex.org/W2101155126","https://openalex.org/W2363545964"],"abstract_inverted_index":{"Driver":[0,87],"classification":[1],"is":[2,17],"used":[3],"recently":[4],"for":[5,51,57,179],"vehicle":[6,30],"anti-burglary":[7],"and":[8,56,110,134,145],"fake":[9],"driver":[10],"accounts":[11],"based":[12],"on":[13,24],"driving":[14,36,92,125],"behavior.":[15],"Anti-burglary":[16],"a":[18,48,81,96],"challenging":[19],"problem":[20,55,178],"as":[21],"it":[22],"leans":[23],"external":[25],"devices":[26],"to":[27,38,46,94,118,141,158],"defend":[28],"against":[29],"theft.":[31],"Several":[32],"researchers":[33],"analyzed":[34],"the":[35,52,69,100,105,111,114,120,130,136,149,176],"behavior":[37,93],"identify":[39,95],"drivers,":[40],"but":[41],"they":[42],"faced":[43],"several":[44],"challenges":[45],"produce":[47],"stable":[49],"model":[50],"cold":[53],"start":[54],"medium-long":[58],"sequences.":[59],"In":[60,76],"addition,":[61],"some":[62],"approaches":[63],"had":[64],"an":[65],"unpleasant":[66],"performance":[67,131],"when":[68],"action":[70],"space":[71],"increased":[72],"(>":[73],"2":[74],"drivers).":[75],"this":[77],"paper,":[78],"we":[79,128],"propose":[80],"novel":[82],"approach":[83,162],"named":[84],"OnlineDC":[85],"(Online":[86],"Classification),":[88],"which":[89],"leverages":[90],"temporal":[91],"human":[97],"subject":[98],"behind":[99],"wheel.":[101],"Our":[102],"method":[103],"utilizes":[104],"Gated":[106],"Recurrent":[107],"Unit":[108],"(GRU)":[109],"ResNet":[112],"with":[113],"Squeeze-Excite":[115],"blocks":[116],"(SE)":[117],"analyze":[119],"long-short":[121],"term":[122],"patterns":[123],"of":[124,152],"behaviors.":[126],"Moreover,":[127],"fostered":[129],"by":[132],"building":[133],"applying":[135],"Feature":[137],"Generation":[138],"(FG)":[139],"algorithm":[140],"extract":[142],"spectral,":[143],"temporal,":[144],"statistical":[146],"features":[147],"from":[148],"sensing":[150],"data":[151],"vehicles.":[153],"We":[154],"conducted":[155],"extensive":[156],"experiments":[157],"show":[159],"how":[160],"our":[161,172],"outperformed":[163],"state-of-the-art":[164],"baseline":[165],"methods.":[166],"The":[167],"results":[168],"also":[169],"showed":[170],"that":[171],"solution":[173],"could":[174],"resolve":[175],"cold-start":[177],"short":[180],"patterns.":[181]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
