{"id":"https://openalex.org/W2963024417","doi":"https://doi.org/10.1109/bigdata.2017.8258162","title":"DxNAT \u2014 Deep neural networks for explaining non-recurring traffic congestion","display_name":"DxNAT \u2014 Deep neural networks for explaining non-recurring traffic congestion","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2963024417","doi":"https://doi.org/10.1109/bigdata.2017.8258162","mag":"2963024417"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258162","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/A5057832802","display_name":"Fangzhou Sun","orcid":"https://orcid.org/0000-0002-8937-146X"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]},{"id":"https://openalex.org/I4210160740","display_name":"Integrated Software (United States)","ror":"https://ror.org/05dp7m259","country_code":"US","type":"company","lineage":["https://openalex.org/I4210160740"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fangzhou Sun","raw_affiliation_strings":["Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I4210160740","https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049776939","display_name":"Abhishek Dubey","orcid":"https://orcid.org/0000-0002-0168-4948"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]},{"id":"https://openalex.org/I4210160740","display_name":"Integrated Software (United States)","ror":"https://ror.org/05dp7m259","country_code":"US","type":"company","lineage":["https://openalex.org/I4210160740"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Dubey","raw_affiliation_strings":["Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I4210160740","https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023026501","display_name":"Jules White","orcid":"https://orcid.org/0000-0002-6331-2365"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]},{"id":"https://openalex.org/I4210160740","display_name":"Integrated Software (United States)","ror":"https://ror.org/05dp7m259","country_code":"US","type":"company","lineage":["https://openalex.org/I4210160740"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jules White","raw_affiliation_strings":["Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I4210160740","https://openalex.org/I200719446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057832802"],"corresponding_institution_ids":["https://openalex.org/I200719446","https://openalex.org/I4210160740"],"apc_list":null,"apc_paid":null,"fwci":4.5679,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.9434623,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2141","last_page":"2150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9955000281333923,"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":0.9952999949455261,"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":0.7308192849159241},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.6361908912658691},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5190870761871338},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5032109618186951},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4999253749847412},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4674806296825409},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.4664819836616516},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.4616936445236206},{"id":"https://openalex.org/keywords/traffic-analysis","display_name":"Traffic analysis","score":0.4613829255104065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44074615836143494},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42020362615585327},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4138241112232208},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.35413724184036255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34228938817977905},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1905823051929474},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12743771076202393},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09949445724487305},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.0768638551235199}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7308192849159241},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6361908912658691},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5190870761871338},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5032109618186951},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4999253749847412},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4674806296825409},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.4664819836616516},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.4616936445236206},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.4613829255104065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44074615836143494},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42020362615585327},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4138241112232208},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35413724184036255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34228938817977905},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1905823051929474},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12743771076202393},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09949445724487305},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0768638551235199},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258162","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":0.5600000023841858,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309151","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W272522870","https://openalex.org/W1766637835","https://openalex.org/W1909515509","https://openalex.org/W1911530584","https://openalex.org/W2021002141","https://openalex.org/W2042850276","https://openalex.org/W2058898885","https://openalex.org/W2060354204","https://openalex.org/W2095635907","https://openalex.org/W2109313784","https://openalex.org/W2114805228","https://openalex.org/W2117618130","https://openalex.org/W2156017603","https://openalex.org/W2157825442","https://openalex.org/W2166619090","https://openalex.org/W2271840356","https://openalex.org/W2401726820","https://openalex.org/W2531524150","https://openalex.org/W2579495707","https://openalex.org/W2584022624","https://openalex.org/W2592443149","https://openalex.org/W2593584390","https://openalex.org/W2743812350","https://openalex.org/W2996489182","https://openalex.org/W3105580975","https://openalex.org/W6609918895","https://openalex.org/W6694517276","https://openalex.org/W6712767530"],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W2374043190","https://openalex.org/W2363298784","https://openalex.org/W2367402697"],"abstract_inverted_index":{"Non-recurring":[0],"traffic":[1,22,27,47,90,101],"congestion":[2,28,91],"is":[3,57,117,128],"caused":[4,92],"by":[5,93],"temporary":[6],"disruptions,":[7],"such":[8],"as":[9,105,130],"accidents,":[10],"sports":[11],"games,":[12],"adverse":[13],"weather,":[14],"etc.":[15],"We":[16,159],"use":[17,145],"data":[18,53,64,113,132],"related":[19],"to":[20,39,60,135,152],"real-time":[21,63],"speed,":[23],"jam":[24],"factors":[25],"(a":[26],"indicator),":[29],"and":[30,65,121,167],"events":[31],"collected":[32],"over":[33,50],"a":[34,41,103,106,125,131],"year":[35],"from":[36,114],"Nashville,":[37],"TN":[38],"train":[40],"multi-layered":[42],"deep":[43],"neural":[44],"network.":[45],"The":[46,55],"dataset":[48],"contains":[49],"900":[51],"million":[52],"records.":[54],"network":[56],"thereafter":[58],"used":[59,129],"classify":[61],"the":[62,100,111,146,154,157,161,164,168],"identify":[66],"anomalous":[67],"operations.":[68],"Compared":[69],"with":[70,119,139],"traditional":[71],"approaches":[72],"of":[73,85,156,163],"using":[74],"statistical":[75],"or":[76],"machine":[77],"learning":[78],"techniques,":[79],"our":[80],"model":[81],"reaches":[82],"an":[83],"accuracy":[84],"98.73":[86],"percent":[87],"when":[88],"identifying":[89],"football":[94],"games.":[95],"Our":[96],"approach":[97],"first":[98],"encodes":[99],"across":[102],"region":[104],"scaled":[107],"image.":[108],"After":[109],"that":[110],"image":[112],"different":[115],"timestamps":[116],"fused":[118],"event-":[120],"time-related":[122],"data.":[123],"Then":[124],"crossover":[126],"operator":[127],"augmentation":[133],"method":[134],"generate":[136],"training":[137,165],"datasets":[138],"more":[140],"balanced":[141],"classes.":[142],"Finally,":[143],"we":[144],"receiver":[147],"operating":[148],"characteristic":[149],"(ROC)":[150],"analysis":[151,162],"tune":[153],"sensitivity":[155],"classifier.":[158],"present":[160],"time":[166,170],"inference":[169],"separately.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
