{"id":"https://openalex.org/W2905171805","doi":"https://doi.org/10.1109/itsc.2018.8569642","title":"Unsupervised Incident Detection Model in Urban and Freeway Networks","display_name":"Unsupervised Incident Detection Model in Urban and Freeway Networks","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2905171805","doi":"https://doi.org/10.1109/itsc.2018.8569642","mag":"2905171805"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569642","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569642","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","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/A5009521221","display_name":"Yaroslav Hernandez-Potiomkin","orcid":"https://orcid.org/0000-0001-8951-4937"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yaroslav Hernandez-Potiomkin","raw_affiliation_strings":["Aimsun SL, R&D department, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Aimsun SL, R&D department, Barcelona, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033438909","display_name":"Mohammad Saifuzzaman","orcid":"https://orcid.org/0000-0003-1342-8762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Saifuzzaman","raw_affiliation_strings":["R&D department, Aimsun Pty Ltd, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"R&D department, Aimsun Pty Ltd, Sydney, NSW, Australia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112026508","display_name":"Emmanuel Bert","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emmanuel Bert","raw_affiliation_strings":["Aimsun SL, Professional Sevices department, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Aimsun SL, Professional Sevices department, Barcelona, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002291518","display_name":"Rafael Mena-Yedra","orcid":"https://orcid.org/0000-0002-7046-6741"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rafael Mena-Yedra","raw_affiliation_strings":["Aimsun SL, R&D department, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Aimsun SL, R&D department, Barcelona, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046632910","display_name":"Tamara Djukic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tamara Djukic","raw_affiliation_strings":["Aimsun SL, R&D department, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Aimsun SL, R&D department, Barcelona, Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103127801","display_name":"Jordi Casas","orcid":"https://orcid.org/0000-0002-7110-8751"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jordi Casas","raw_affiliation_strings":["Aimsun SL, R&D department, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Aimsun SL, R&D department, Barcelona, Spain","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5009521221"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9419,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77695449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987000226974487,"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":0.9987000226974487,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9901000261306763,"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/T11106","display_name":"Data Management and Algorithms","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7522193789482117},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.7113077044487},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6935896873474121},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6347286105155945},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4979984760284424},{"id":"https://openalex.org/keywords/incident-management","display_name":"Incident management","score":0.4658777415752411},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4365465044975281},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41286394000053406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.342973530292511},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12489756941795349},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0796678364276886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7522193789482117},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.7113077044487},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6935896873474121},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6347286105155945},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4979984760284424},{"id":"https://openalex.org/C2780952636","wikidata":"https://www.wikidata.org/wiki/Q13479512","display_name":"Incident management","level":2,"score":0.4658777415752411},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4365465044975281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41286394000053406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.342973530292511},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12489756941795349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0796678364276886},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2018.8569642","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569642","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W134352601","https://openalex.org/W178248616","https://openalex.org/W774886473","https://openalex.org/W1506806321","https://openalex.org/W1507697757","https://openalex.org/W1975138410","https://openalex.org/W1992984926","https://openalex.org/W1998407189","https://openalex.org/W2014594345","https://openalex.org/W2042759501","https://openalex.org/W2043829247","https://openalex.org/W2049472095","https://openalex.org/W2069581686","https://openalex.org/W2107806935","https://openalex.org/W2128558188","https://openalex.org/W2129819572","https://openalex.org/W2623832105","https://openalex.org/W3150501812","https://openalex.org/W4238796079","https://openalex.org/W4256155966","https://openalex.org/W6607264821","https://openalex.org/W6630452646","https://openalex.org/W6744693602"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W2355463328","https://openalex.org/W2402648945","https://openalex.org/W939486154","https://openalex.org/W1431147547","https://openalex.org/W2169858051","https://openalex.org/W1968139932","https://openalex.org/W2771614534","https://openalex.org/W2739722019","https://openalex.org/W205019911"],"abstract_inverted_index":{"Reducing":[0],"the":[1,71,86,92,124],"effects":[2],"of":[3,11,54],"incidents":[4],"by":[5],"their":[6,81,146],"early":[7,132],"detection":[8,23,134],"is":[9,43,106],"one":[10],"a":[12,35,39,46],"crucial":[13],"requirements":[14],"for":[15,108],"incident":[16,22,98,133],"management.":[17],"This":[18],"paper":[19],"presents":[20],"automated":[21],"model":[24,36,74,104],"based":[25],"on":[26],"unsupervised":[27],"approach":[28],"that":[29,75,123],"uses":[30],"only":[31],"traffic":[32,55,93,101],"observations":[33],"as":[34,45,68,141],"inputs.":[37],"First,":[38],"novel":[40],"self-tuning":[41],"statistic":[42],"introduced":[44],"feature":[47],"generation":[48],"function":[49],"to":[50,89,96,136],"capture":[51],"spatio-temporal":[52],"relationship":[53],"data":[56,78,115],"in":[57,70],"both,":[58],"urban":[59,117],"and":[60,80,116,131,145],"freeway":[61,112],"networks.":[62],"Next,":[63],"these":[64],"features":[65],"are":[66],"used":[67,138],"input":[69],"segment-based":[72],"mixture":[73],"learns":[76],"complex":[77],"distributions":[79],"parameters.":[82],"Then,":[83],"we":[84],"use":[85],"Mahalanobis":[87],"distance":[88],"determine":[90],"whether":[91],"observation":[94],"corresponds":[95],"an":[97],"or":[99],"recurrent":[100],"state.":[102],"The":[103],"performance":[105],"demonstrated":[107],"two":[109],"network":[110],"examples,":[111],"with":[113,118],"real":[114],"simulated":[119],"data.":[120],"Results":[121],"show":[122],"developed":[125],"method":[126],"achieves":[127],"high":[128],"accuracy":[129],"rates":[130],"compared":[135],"widely":[137],"approaches,":[139],"such":[140],"California":[142],"algorithm":[143],"series":[144],"extensions.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}