{"id":"https://openalex.org/W4220787802","doi":"https://doi.org/10.1109/access.2022.3161835","title":"Automated Traffic Incident Detection: Coping With Imbalanced and Small Datasets","display_name":"Automated Traffic Incident Detection: Coping With Imbalanced and Small Datasets","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4220787802","doi":"https://doi.org/10.1109/access.2022.3161835"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3161835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3161835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09740116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09740116.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036252940","display_name":"Tian Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I119203015","display_name":"Shandong University of Technology","ror":"https://ror.org/02mr3ar13","country_code":"CN","type":"education","lineage":["https://openalex.org/I119203015"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Xie","raw_affiliation_strings":["School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China"],"raw_orcid":"https://orcid.org/0000-0001-8902-5850","affiliations":[{"raw_affiliation_string":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China","institution_ids":["https://openalex.org/I119203015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014805708","display_name":"Qiang Shang","orcid":"https://orcid.org/0000-0002-0016-2232"},"institutions":[{"id":"https://openalex.org/I119203015","display_name":"Shandong University of Technology","ror":"https://ror.org/02mr3ar13","country_code":"CN","type":"education","lineage":["https://openalex.org/I119203015"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Shang","raw_affiliation_strings":["School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China"],"raw_orcid":"https://orcid.org/0000-0002-0016-2232","affiliations":[{"raw_affiliation_string":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China","institution_ids":["https://openalex.org/I119203015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057987365","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0001-7505-0393"},"institutions":[{"id":"https://openalex.org/I119203015","display_name":"Shandong University of Technology","ror":"https://ror.org/02mr3ar13","country_code":"CN","type":"education","lineage":["https://openalex.org/I119203015"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China","institution_ids":["https://openalex.org/I119203015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I119203015"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6175,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.80636605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"35521","last_page":"35540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7025206685066223},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.6009851694107056},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5279264450073242},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.5245221853256226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4550853371620178},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4535750150680542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4071480929851532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.348847895860672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025206685066223},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.6009851694107056},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5279264450073242},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5245221853256226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4550853371620178},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4535750150680542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4071480929851532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.348847895860672},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3161835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3161835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09740116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:54fe8711bc8d415db96443cf4bc9238d","is_oa":false,"landing_page_url":"https://doaj.org/article/54fe8711bc8d415db96443cf4bc9238d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 35521-35540 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3161835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3161835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09740116.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5251368888","display_name":null,"funder_award_id":"ZR2021MF109","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220787802.pdf","grobid_xml":"https://content.openalex.org/works/W4220787802.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W774886473","https://openalex.org/W1507697757","https://openalex.org/W1520514216","https://openalex.org/W1548826882","https://openalex.org/W1746041559","https://openalex.org/W1968010607","https://openalex.org/W1975138410","https://openalex.org/W2005133178","https://openalex.org/W2005599048","https://openalex.org/W2013875843","https://openalex.org/W2027796594","https://openalex.org/W2043829247","https://openalex.org/W2054332472","https://openalex.org/W2061499892","https://openalex.org/W2064236159","https://openalex.org/W2079018711","https://openalex.org/W2094649913","https://openalex.org/W2099788790","https://openalex.org/W2101083585","https://openalex.org/W2107806935","https://openalex.org/W2148143831","https://openalex.org/W2164330572","https://openalex.org/W2166478604","https://openalex.org/W2249423915","https://openalex.org/W2603530161","https://openalex.org/W2900362095","https://openalex.org/W2989993634","https://openalex.org/W3007204104","https://openalex.org/W3016229397","https://openalex.org/W3036417315","https://openalex.org/W3080948845","https://openalex.org/W3086129031","https://openalex.org/W3086984564","https://openalex.org/W3116271006","https://openalex.org/W6630452646","https://openalex.org/W6632821843","https://openalex.org/W6637891111","https://openalex.org/W6691089252"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W4401045170","https://openalex.org/W3172259201","https://openalex.org/W2903618681","https://openalex.org/W3184937791","https://openalex.org/W4388745254","https://openalex.org/W2980082554","https://openalex.org/W1517228774","https://openalex.org/W2767419625","https://openalex.org/W2389704471"],"abstract_inverted_index":{"Automatic":[0],"incident":[1,28,47,73,284],"detection":[2,48,81,212],"(AID)":[3],"has":[4,239,277],"always":[5],"been":[6],"one":[7],"of":[8,15,24,114,122,132,153,162,256],"the":[9,13,20,68,109,120,136,144,151,160,172,176,180,232,257,261,272,274],"focus":[10],"issues":[11],"in":[12,76],"field":[14],"transportation.":[16],"However,":[17],"due":[18],"to":[19,72,126,142,191,201],"contingency":[21],"and":[22,32,52,62,102,111,220,227,231,280],"randomness":[23],"traffic":[25,27,46,115,193,283],"incident,":[26],"samples":[29,156],"are":[30,117,169,199,248,260],"scarce":[31],"far":[33],"less":[34],"than":[35],"non-incident":[36],"samples.":[37,165],"Therefore,":[38,88],"unlike":[39],"other":[40,265],"scenarios":[41],"using":[42,96,175],"large-scale":[43],"deep":[44],"networks,":[45],"tackle":[49],"at":[50],"small":[51,57],"imbalanced":[53,146],"sample":[54,58,147],"size.":[55],"Imbalanced,":[56],"data":[59],"sets,":[60],"inappropriate":[61],"incomplete":[63],"initial":[64,124,133,173],"variable":[65],"sets":[66],"make":[67],"AID":[69,91],"model":[70,78,203,238,259,276],"insensitive":[71],"samples,":[74],"resulting":[75],"unsatisfactory":[77],"performance":[79],"(low":[80],"rate":[82,210,213],"or":[83],"high":[84],"false":[85,208],"alarm":[86,209],"rate).":[87],"a":[89,128,278],"hybrid":[90],"method":[92,139],"(SASYNO-RF-RSKNN)":[93],"is":[94,140,157,189,243],"proposed":[95,258,275],"self-adaptive":[97],"synthetic":[98],"over-sampling,":[99],"random":[100,103],"forest":[101],"subspace":[104],"k":[105],"nearest":[106],"neighbor.":[107],"First,":[108],"spatial-temporal":[110],"real-time":[112],"characteristics":[113],"stream":[116],"used":[118,141,200],"for":[119,282],"selection":[121],"appropriate":[123],"variables":[125,168,174,186],"construct":[127],"relatively":[129],"complete":[130],"set":[131],"variables.":[134],"Second,":[135],"SASYNO":[137],"oversampling":[138],"expand":[143],"original":[145],"database,":[148],"so":[149],"that":[150,236,246],"number":[152,161],"minority":[154],"class":[155,164],"consistent":[158],"with":[159,184,264],"most":[163],"Then,":[166],"feature":[167,185],"selected":[170],"from":[171],"RF":[177],"algorithm.":[178],"Finally,":[179],"RSKNN":[181],"ensemble":[182],"algorithm":[183],"as":[187],"input":[188],"employed":[190],"detect":[192],"incident.":[194],"In":[195],"addition,":[196],"six":[197],"indexes":[198,255],"evaluate":[202],"performance,":[204],"including":[205],"accuracy":[206],"(ACC),":[207],"(FAR),":[211],"(DR),":[214],"precision,":[215],"Matthews":[216],"correlation":[217],"coefficient":[218],"(MCC)":[219],"F1-score.":[221],"Simultaneously,":[222],"we":[223],"also":[224],"designed":[225],"horizontal":[226],"vertical":[228],"contrast":[229],"experiments,":[230],"experimental":[233],"results":[234],"show":[235],"SASYNO-RF-RSKNN":[237],"superior":[240],"performance.":[241],"It":[242],"worth":[244],"mentioning":[245],"experiments":[247],"implemented":[249],"on":[250],"two":[251],"real-world":[252],"datasets.":[253],"Most":[254],"best":[262],"compared":[263],"five":[266],"excellent":[267],"machine":[268],"learning":[269],"algorithms.":[270],"On":[271],"whole,":[273],"dependable":[279],"high-performance":[281],"detection.":[285]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-30T13:55:48.251075","created_date":"2025-10-10T00:00:00"}