{"id":"https://openalex.org/W2784317170","doi":"https://doi.org/10.1109/bigdata.2017.8258561","title":"Road safety estimation utilizing big and heterogeneous vehicle recorder data","display_name":"Road safety estimation utilizing big and heterogeneous vehicle recorder data","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2784317170","doi":"https://doi.org/10.1109/bigdata.2017.8258561","mag":"2784317170"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5005790090","display_name":"Masashi Toyoda","orcid":"https://orcid.org/0000-0001-9473-5531"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Masashi Toyoda","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109280433","display_name":"Daisaku Yokoyama","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daisaku Yokoyama","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038978456","display_name":"Junpei Komiyama","orcid":"https://orcid.org/0000-0003-0095-6558"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junpei Komiyama","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109126172","display_name":"Masahiko Itoh","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiko Itoh","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5005790090"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.27335284,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4841","last_page":"4842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10370","display_name":"Traffic and Road Safety","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9977999925613403,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9973999857902527,"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/dash","display_name":"Dash","score":0.741989254951477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7233012914657593},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5213440656661987},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.49305102229118347},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45659172534942627},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4218006730079651},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.4044235944747925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3785991072654724},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3480597734451294},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32057657837867737},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2917978763580322},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21534430980682373},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1387081742286682},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0748475193977356},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.06733730435371399}],"concepts":[{"id":"https://openalex.org/C2776090536","wikidata":"https://www.wikidata.org/wiki/Q187819","display_name":"Dash","level":2,"score":0.741989254951477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233012914657593},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5213440656661987},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.49305102229118347},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45659172534942627},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4218006730079651},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.4044235944747925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3785991072654724},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3480597734451294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32057657837867737},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2917978763580322},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21534430980682373},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1387081742286682},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0748475193977356},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06733730435371399},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2058816802","https://openalex.org/W2196640000","https://openalex.org/W2295598076","https://openalex.org/W2366582931","https://openalex.org/W2613718673","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W4399050431","https://openalex.org/W1603294850","https://openalex.org/W3123507829","https://openalex.org/W1558801379","https://openalex.org/W4376486551","https://openalex.org/W2361827796","https://openalex.org/W3172060415","https://openalex.org/W3166484078","https://openalex.org/W2290529631","https://openalex.org/W2603355878"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,53],"method":[3,72],"for":[4],"estimating":[5],"road":[6,33],"safety":[7],"using":[8],"large-scale":[9],"and":[10,39,76],"heterogeneous":[11],"vehicle":[12],"recorder":[13],"data.":[14],"Our":[15],"assumption":[16],"in":[17,52],"this":[18],"study":[19],"is":[20],"that":[21,70],"occurrences":[22],"of":[23],"traffic":[24],"accidents":[25],"on":[26,64],"crossings":[27,62],"could":[28],"be":[29],"estimated":[30],"by":[31,42],"their":[32],"shape,":[34],"driving":[35],"behaviors":[36],"over":[37],"them,":[38],"photos":[40],"taken":[41],"dash":[43],"cams.":[44],"Utilizing":[45],"3":[46],"years":[47],"dataset":[48],"from":[49],"1,500":[50],"drivers":[51],"fleet":[54],"delivery":[55],"company,":[56],"we":[57],"try":[58],"to":[59],"detect":[60],"risky":[61,80],"based":[63],"supervised":[65],"machine":[66],"learning.":[67],"Results":[68],"show":[69],"our":[71],"provides":[73],"good":[74],"performance,":[75],"can":[77],"extract":[78],"potentially":[79],"crossings.":[81]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}