{"id":"https://openalex.org/W4307335299","doi":"https://doi.org/10.1145/3558819.3565146","title":"Correlation Analysis of Traffic Accident Factors based on Mean Clustering","display_name":"Correlation Analysis of Traffic Accident Factors based on Mean Clustering","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4307335299","doi":"https://doi.org/10.1145/3558819.3565146"},"language":"en","primary_location":{"id":"doi:10.1145/3558819.3565146","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558819.3565146","pdf_url":null,"source":{"id":"https://openalex.org/S4363608855","display_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","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 7th International Conference on Cyber Security and Information Engineering","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/A5085345806","display_name":"Ziwen Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziwen Niu","raw_affiliation_strings":["Henan University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Science and Technology, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413382","display_name":"Yanli Wang","orcid":"https://orcid.org/0009-0007-5480-3158"},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanli Wang","raw_affiliation_strings":["Henan University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Science and Technology, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054584658","display_name":"Shibao Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shibao Sun","raw_affiliation_strings":["Henan University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Science and Technology, China","institution_ids":["https://openalex.org/I167383011"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085345806"],"corresponding_institution_ids":["https://openalex.org/I167383011"],"apc_list":null,"apc_paid":null,"fwci":0.6746,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62315876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"569","last_page":"575"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9944999814033508,"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":0.9944999814033508,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7861665487289429},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.6580281257629395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6535077095031738},{"id":"https://openalex.org/keywords/traffic-accident","display_name":"Traffic accident","score":0.6363027691841125},{"id":"https://openalex.org/keywords/accident","display_name":"Accident (philosophy)","score":0.6338944435119629},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6140873432159424},{"id":"https://openalex.org/keywords/apriori-algorithm","display_name":"Apriori algorithm","score":0.5216289162635803},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.48664841055870056},{"id":"https://openalex.org/keywords/clarity","display_name":"CLARITY","score":0.46209731698036194},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.258797287940979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2516489624977112},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1623564064502716},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.1497586965560913}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7861665487289429},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.6580281257629395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6535077095031738},{"id":"https://openalex.org/C2989506057","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Traffic accident","level":2,"score":0.6363027691841125},{"id":"https://openalex.org/C2780289543","wikidata":"https://www.wikidata.org/wiki/Q424630","display_name":"Accident (philosophy)","level":2,"score":0.6338944435119629},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140873432159424},{"id":"https://openalex.org/C81440476","wikidata":"https://www.wikidata.org/wiki/Q513511","display_name":"Apriori algorithm","level":3,"score":0.5216289162635803},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.48664841055870056},{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.46209731698036194},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.258797287940979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2516489624977112},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1623564064502716},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.1497586965560913},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3558819.3565146","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558819.3565146","pdf_url":null,"source":{"id":"https://openalex.org/S4363608855","display_name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","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 7th International Conference on Cyber Security and Information Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2793073333","https://openalex.org/W3087111763","https://openalex.org/W3096681546","https://openalex.org/W3217195399"],"related_works":["https://openalex.org/W2390051172","https://openalex.org/W2297208791","https://openalex.org/W2367209111","https://openalex.org/W2351000793","https://openalex.org/W2366790077","https://openalex.org/W2348276166","https://openalex.org/W3034345083","https://openalex.org/W2607264580","https://openalex.org/W3012205960","https://openalex.org/W1483188779"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,45,51,70],"effectively":[3],"improve":[4],"the":[5,14,30,33,37,47,52,61,72,79,99,103,111,118],"mining":[6,17,88,100],"efficiency":[7,101],"of":[8,16,54,102],"traffic":[9,130],"accident":[10,34,39,48,59,121,124,131],"factors":[11,122],"and":[12,41,85,92,110,123],"enhance":[13],"clarity":[15],"results,":[18],"an":[19],"association":[20,68],"analysis":[21,69],"method":[22,31],"based":[23],"on":[24,57],"mean":[25],"clustering":[26,44],"is":[27,65,127],"proposed.":[28],"Firstly,":[29],"generalizes":[32],"data,":[35],"extracts":[36],"main":[38,73],"attributes,":[40],"uses":[42,78],"K-means":[43],"classify":[46],"level":[49],"according":[50],"number":[53],"casualties;":[55],"Based":[56],"different":[58],"levels,":[60],"improved":[62],"Apriori":[63],"algorithm":[64,105],"used":[66],"for":[67,90,129],"mine":[71],"contributing":[74],"factors.":[75],"The":[76,95],"experiment":[77],"British":[80],"government":[81],"public":[82],"data":[83,87],"set":[84],"multiple":[86],"algorithms":[89],"quantitative":[91],"qualitative":[93],"analysis.":[94,133],"results":[96,113],"show":[97],"that":[98],"combined":[104],"has":[106],"been":[107],"significantly":[108],"improved,":[109],"correlation":[112],"can":[114],"more":[115],"intuitively":[116],"reflect":[117],"relationship":[119],"between":[120],"severity,":[125],"which":[126],"suitable":[128],"profile":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
