{"id":"https://openalex.org/W2990371752","doi":"https://doi.org/10.1109/itsc.2019.8917030","title":"Feature Selection for Prediction of Railway Disruption Length","display_name":"Feature Selection for Prediction of Railway Disruption Length","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990371752","doi":"https://doi.org/10.1109/itsc.2019.8917030","mag":"2990371752"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2019.8917030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (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/A5072459419","display_name":"Menglin Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Menglin Fan","raw_affiliation_strings":["National Research Center of Railway Safety Assessment, Beijing Jiao-tong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Research Center of Railway Safety Assessment, Beijing Jiao-tong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101513399","display_name":"Wei Zheng","orcid":"https://orcid.org/0000-0001-8921-5304"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zheng","raw_affiliation_strings":["National Research Center of Railway Safety Assessment, Beijing Jiao-tong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Research Center of Railway Safety Assessment, Beijing Jiao-tong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072459419"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.17085018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"351","last_page":"356"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9868999719619751,"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":0.9868999719619751,"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/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9591000080108643,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14368","display_name":"Evaluation and Optimization Models","score":0.9538999795913696,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5920259356498718},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5908753275871277},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5768254995346069},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4501476585865021},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4185664653778076},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4157021939754486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3681461215019226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3655834197998047},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3505803346633911},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.32736480236053467},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3211917281150818},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2436911165714264},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24070122838020325}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5920259356498718},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5908753275871277},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5768254995346069},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4501476585865021},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4185664653778076},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4157021939754486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3681461215019226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3655834197998047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3505803346633911},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.32736480236053467},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3211917281150818},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2436911165714264},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24070122838020325},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2019.8917030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2058580716","https://openalex.org/W2064675550","https://openalex.org/W2129059885","https://openalex.org/W2157331557","https://openalex.org/W2165700458","https://openalex.org/W2342408036","https://openalex.org/W2342843807","https://openalex.org/W2404648423","https://openalex.org/W2508428251","https://openalex.org/W2596867035","https://openalex.org/W2759474451","https://openalex.org/W2800900810","https://openalex.org/W2884867318","https://openalex.org/W2901078833","https://openalex.org/W2904213626","https://openalex.org/W2904917608","https://openalex.org/W2914208492","https://openalex.org/W2962874036","https://openalex.org/W2963351448"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W3147584709","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2977677679","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Disruptions":[0],"such":[1],"as":[2],"accidents,":[3],"facility":[4],"failures":[5],"and":[6,21,69,94,115],"power":[7],"outage":[8],"occur":[9],"frequently":[10],"during":[11],"daily":[12],"railway":[13,19,54,80],"operation,":[14],"which":[15],"can":[16,36,116,137],"disrupt":[17],"the":[18,38,43,50,78,88,101,109,127,133,140,143],"timetable":[20],"cause":[22],"financial":[23],"cost":[24],"in":[25,58],"terms":[26],"of":[27,45,53,129,142],"lost":[28],"patronage.":[29],"Recent":[30],"study":[31],"indicates":[32],"that":[33,108],"skilled":[34],"dispatchers":[35],"estimate":[37],"disruption":[39,55,89],"length":[40,56],"based":[41],"on":[42,121],"analysis":[44],"related":[46,102],"features.":[47,104],"To":[48],"select":[49,100],"significant":[51],"features":[52],"(RDL),":[57],"this":[59],"study,":[60],"bidirectional":[61],"gated":[62],"recurrent":[63],"unit":[64],"with":[65,123],"focal":[66],"loss":[67],"(FL-BiGRU)":[68],"maximal":[70],"information":[71,135],"coefficient":[72,136],"(MIC)":[73],"were":[74],"applied":[75,98],"to":[76,86,99],"analyze":[77],"Chinese":[79],"accident":[81],"reports.":[82],"FL-BiGRU":[83,110],"was":[84,97],"proposed":[85],"classify":[87],"causes":[90],"into":[91],"seven":[92],"categories":[93,122],"MIC":[95],"algorithm":[96],"RDL":[103],"Experiment":[105],"results":[106],"showed":[107],"model":[111],"outperforms":[112],"rest":[113],"models":[114],"provide":[117],"good":[118],"accuracy":[119,141],"especially":[120],"fewer":[124],"samples.":[125],"Moreover,":[126],"set":[128],"predictors":[130],"built":[131],"by":[132],"maximum":[134],"significantly":[138],"improve":[139],"prediction":[144],"model.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}