{"id":"https://openalex.org/W4398186359","doi":"https://doi.org/10.1145/3605098.3636162","title":"Autoencoder-based Continual Outlier Correlation Detection for Real-Time Traffic Flow Prediction","display_name":"Autoencoder-based Continual Outlier Correlation Detection for Real-Time Traffic Flow Prediction","publication_year":2024,"publication_date":"2024-04-08","ids":{"openalex":"https://openalex.org/W4398186359","doi":"https://doi.org/10.1145/3605098.3636162"},"language":"en","primary_location":{"id":"doi:10.1145/3605098.3636162","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3605098.3636162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.tue.nl/en/publications/f5b0979a-2d44-438f-9202-467d7aaec65d","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061601479","display_name":"Himanshu Choudhary","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Himanshu Choudhary","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, Netherlands"],"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114166415","display_name":"Marwan Hassani","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marwan Hassani","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, Netherlands"],"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061601479"],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":1.4464,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83972535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"218","last_page":"220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9977999925613403,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.994700014591217,"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/autoencoder","display_name":"Autoencoder","score":0.8722631335258484},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6699002981185913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.664958119392395},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5678603053092957},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5640940070152283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4815707206726074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39312291145324707},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3828253149986267},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16625499725341797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12071588635444641}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8722631335258484},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6699002981185913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.664958119392395},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5678603053092957},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5640940070152283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4815707206726074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39312291145324707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3828253149986267},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16625499725341797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12071588635444641},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3605098.3636162","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3605098.3636162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire/f5b0979a-2d44-438f-9202-467d7aaec65d","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/f5b0979a-2d44-438f-9202-467d7aaec65d","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Choudhary, H & Hassani, M 2024, Autoencoder-based Continual Outlier Correlation Detection for Real-Time Traffic Flow Prediction. in SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. Association for Computing Machinery, Inc., New York, pp. 218-220, 39th Annual ACM Symposium on Applied Computing, SAC 2024, Avila, Spain, 8/04/24. https://doi.org/10.1145/3605098.3636162","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/f5b0979a-2d44-438f-9202-467d7aaec65d","is_oa":true,"landing_page_url":"https://research.tue.nl/files/350957103/3605098.3636162.pdf","pdf_url":"https://pure.tue.nl/ws/files/350957103/3605098.3636162.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Choudhary, H & Hassani, M 2024, Autoencoder-based Continual Outlier Correlation Detection for Real-Time Traffic Flow Prediction. in SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. Association for Computing Machinery, Inc., New York, pp. 218-220, 39th Annual ACM Symposium on Applied Computing, SAC 2024, Avila, Spain, 8/04/24. https://doi.org/10.1145/3605098.3636162","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire/f5b0979a-2d44-438f-9202-467d7aaec65d","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/f5b0979a-2d44-438f-9202-467d7aaec65d","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Choudhary, H & Hassani, M 2024, Autoencoder-based Continual Outlier Correlation Detection for Real-Time Traffic Flow Prediction. in SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. Association for Computing Machinery, Inc., New York, pp. 218-220, 39th Annual ACM Symposium on Applied Computing, SAC 2024, Avila, Spain, 8/04/24. https://doi.org/10.1145/3605098.3636162","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2114015015","https://openalex.org/W2904996768","https://openalex.org/W2930989721","https://openalex.org/W2971503667","https://openalex.org/W2978506316","https://openalex.org/W3097309396","https://openalex.org/W3200209198","https://openalex.org/W4224316504","https://openalex.org/W4254182148","https://openalex.org/W4293057635","https://openalex.org/W4302369416","https://openalex.org/W4327656422","https://openalex.org/W4360982721"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W3186512740","https://openalex.org/W2499612753","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729"],"abstract_inverted_index":{"In":[0],"urban":[1],"landscapes,":[2],"traffic":[3,23,30,51,101],"congestion,":[4],"often":[5],"identified":[6],"by":[7,42],"outlier":[8,47],"events":[9],"like":[10],"accidents":[11],"or":[12],"constructions,":[13],"poses":[14],"a":[15,85],"significant":[16],"challenge.":[17],"These":[18,62],"outliers":[19],"result":[20],"in":[21,100],"abrupt":[22],"fluctuations,":[24],"necessitating":[25],"real-time":[26,81],"modeling":[27],"for":[28,45,95],"accurate":[29],"predictions.":[31],"The":[32,112],"proposed":[33],"Outlier":[34],"Weighted":[35],"Autoencoder":[36],"Modeling":[37],"(OWAM)":[38],"framework":[39,113],"addresses":[40],"this":[41],"employing":[43],"autoencoders":[44],"local":[46],"detection":[48],"at":[49],"each":[50],"sensor":[52],"and":[53,78,89,114],"generating":[54],"correlation":[55],"scores":[56],"to":[57,106],"assess":[58],"neighboring":[59,72],"traffic's":[60],"impact.":[61],"scores,":[63],"which":[64],"serve":[65],"as":[66],"the":[67,71,75,107],"weighted":[68],"information":[69],"of":[70,109],"sensors,":[73],"enhance":[74],"model's":[76],"performances":[77],"enable":[79],"effective":[80],"updates.":[82],"OWAM":[83],"achieves":[84],"balance":[86],"between":[87],"accuracy":[88],"efficiency,":[90],"making":[91],"it":[92],"highly":[93],"suitable":[94],"real-world":[96],"applications.":[97],"This":[98],"advancement":[99],"prediction":[102],"models":[103],"significantly":[104],"contributes":[105],"field":[108],"transportation":[110],"management.":[111],"its":[115],"datasets":[116],"are":[117],"publicly":[118],"available":[119],"under":[120],"https://github.com/himanshudce/OWAM.":[121]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
