{"id":"https://openalex.org/W4319778530","doi":"https://doi.org/10.1109/dsaa54385.2022.10032371","title":"Multi-Parameter Forecasting of Fatalities in Road Traffic Accidents","display_name":"Multi-Parameter Forecasting of Fatalities in Road Traffic Accidents","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319778530","doi":"https://doi.org/10.1109/dsaa54385.2022.10032371"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032371","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032371","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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/A5057412360","display_name":"Ritam Nandi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129753","display_name":"Applied Spectra (United States)","ror":"https://ror.org/0343ms580","country_code":"US","type":"company","lineage":["https://openalex.org/I4210129753"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ritam Nandi","raw_affiliation_strings":["Fremont,California","Fremont, California"],"affiliations":[{"raw_affiliation_string":"Fremont,California","institution_ids":["https://openalex.org/I4210129753"]},{"raw_affiliation_string":"Fremont, California","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003994673","display_name":"S. Sri Lakshmi Prasanna","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129753","display_name":"Applied Spectra (United States)","ror":"https://ror.org/0343ms580","country_code":"US","type":"company","lineage":["https://openalex.org/I4210129753"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddaarth Prasanna","raw_affiliation_strings":["Fremont,California","Fremont, California"],"affiliations":[{"raw_affiliation_string":"Fremont,California","institution_ids":["https://openalex.org/I4210129753"]},{"raw_affiliation_string":"Fremont, California","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067468381","display_name":"Prajit Rajkumar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129753","display_name":"Applied Spectra (United States)","ror":"https://ror.org/0343ms580","country_code":"US","type":"company","lineage":["https://openalex.org/I4210129753"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prajit Rajkumar","raw_affiliation_strings":["Fremont,California","Fremont, California"],"affiliations":[{"raw_affiliation_string":"Fremont,California","institution_ids":["https://openalex.org/I4210129753"]},{"raw_affiliation_string":"Fremont, California","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057412360"],"corresponding_institution_ids":["https://openalex.org/I4210129753"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37675607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.963100016117096,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9330000281333923,"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":0.6543494462966919},{"id":"https://openalex.org/keywords/road-accident","display_name":"Road accident","score":0.5942919254302979},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.5708566308021545},{"id":"https://openalex.org/keywords/road-traffic-accident","display_name":"Road traffic accident","score":0.44607263803482056},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43124687671661377},{"id":"https://openalex.org/keywords/traffic-accident","display_name":"Traffic accident","score":0.43088996410369873},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.40493571758270264},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21067050099372864}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543494462966919},{"id":"https://openalex.org/C3018122277","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Road accident","level":2,"score":0.5942919254302979},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.5708566308021545},{"id":"https://openalex.org/C2987776215","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Road traffic accident","level":3,"score":0.44607263803482056},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43124687671661377},{"id":"https://openalex.org/C2989506057","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Traffic accident","level":2,"score":0.43088996410369873},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.40493571758270264},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21067050099372864},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032371","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032371","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W32080805","https://openalex.org/W1552189656","https://openalex.org/W2999309192","https://openalex.org/W4210524907","https://openalex.org/W4223918474","https://openalex.org/W6601284787"],"related_works":["https://openalex.org/W2354557213","https://openalex.org/W1984953192","https://openalex.org/W2413438253","https://openalex.org/W2348375046","https://openalex.org/W2313945969","https://openalex.org/W3082624150","https://openalex.org/W2104758308","https://openalex.org/W3204358079","https://openalex.org/W2366815864","https://openalex.org/W114543423"],"abstract_inverted_index":{"Road":[0],"Traffic":[1],"Accidents":[2],"are":[3],"one":[4],"of":[5,10,34,43,66],"the":[6,41,64,76,81,87,102,108],"major":[7],"preventable":[8],"causes":[9],"fatalities":[11],"globally.":[12],"In":[13],"this":[14],"project,":[15],"we":[16,115],"aim":[17],"to":[18,22,45,56,71,128],"use":[19],"a":[20,32,91],"model":[21],"predict":[23],"whether":[24],"an":[25],"accident":[26],"will":[27],"be":[28],"fatal":[29],"based":[30],"on":[31],"set":[33],"defining":[35],"parameters.":[36],"This":[37],"would":[38,74],"help":[39,52],"in":[40,80],"allocation":[42],"resources":[44],"improve":[46],"road":[47],"safety":[48],"as":[49,51,122],"well":[50,123],"emergency":[53],"medical":[54],"services":[55],"prioritize":[57],"certain":[58],"accidents":[59],"over":[60,120],"others.":[61],"We":[62,85],"tested":[63],"efficacy":[65],"multiple":[67],"machine":[68],"learning":[69],"algorithms":[70],"determine":[72],"which":[73,99],"provide":[75],"most":[77,82,88],"accurate":[78],"results":[79],"optimal":[83],"manner.":[84],"found":[86],"success":[89,118],"using":[90],"k-nearest":[92],"neighbors":[93],"(KNN)":[94],"model,":[95],"with":[96],"99.6%":[97],"accuracy,":[98],"was":[100],"also":[101],"least":[103,109],"computationally":[104],"expensive":[105],"and":[106,132],"had":[107,117],"false":[110],"negatives.":[111],"The":[112],"other":[113],"methods":[114],"implemented":[116],"rates":[119],"90%":[121],"but":[124],"were":[125],"unusable":[126],"due":[127],"their":[129],"low":[130],"precision":[131],"recall.":[133]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}