{"id":"https://openalex.org/W4378804785","doi":"https://doi.org/10.1109/mits.2023.3274787","title":"Gaussian Noise Data Augmentation-Based Delay Prediction for High-Speed Railways","display_name":"Gaussian Noise Data Augmentation-Based Delay Prediction for High-Speed Railways","publication_year":2023,"publication_date":"2023-05-31","ids":{"openalex":"https://openalex.org/W4378804785","doi":"https://doi.org/10.1109/mits.2023.3274787"},"language":"en","primary_location":{"id":"doi:10.1109/mits.2023.3274787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mits.2023.3274787","pdf_url":null,"source":{"id":"https://openalex.org/S131000621","display_name":"IEEE Intelligent Transportation Systems Magazine","issn_l":"1939-1390","issn":["1939-1390","1941-1197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Intelligent Transportation Systems Magazine","raw_type":"journal-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/A5046146732","display_name":"Hairong Dong","orcid":"https://orcid.org/0000-0003-4369-3401"},"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":"Hairong Dong","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668739","display_name":"Jinxing Wang","orcid":"https://orcid.org/0009-0003-0174-6277"},"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":"Jinxing Wang","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-0174-6277","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083475004","display_name":"Xingtang Wu","orcid":"https://orcid.org/0000-0002-3444-0026"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingtang Wu","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3444-0026","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090290928","display_name":"Min Zhou","orcid":"https://orcid.org/0000-0002-9286-1726"},"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":"Min Zhou","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9286-1726","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027725400","display_name":"Jinhu L\u00fc","orcid":"https://orcid.org/0000-0003-0275-8387"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhu L\u00fc","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0275-8387","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.294,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.88886554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"15","issue":"6","first_page":"8","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.9980999827384949,"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/computer-science","display_name":"Computer science","score":0.6931570172309875},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6483160853385925},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5565400719642639},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5301949381828308},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5151468515396118},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5098347067832947},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.46606117486953735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4569019675254822},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45113664865493774},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.437540203332901},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41308867931365967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.377285897731781},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3524709939956665},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24949923157691956},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07824179530143738}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6931570172309875},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6483160853385925},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5565400719642639},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5301949381828308},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5151468515396118},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5098347067832947},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.46606117486953735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4569019675254822},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45113664865493774},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.437540203332901},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41308867931365967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.377285897731781},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3524709939956665},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24949923157691956},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07824179530143738},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mits.2023.3274787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mits.2023.3274787","pdf_url":null,"source":{"id":"https://openalex.org/S131000621","display_name":"IEEE Intelligent Transportation Systems Magazine","issn_l":"1939-1390","issn":["1939-1390","1941-1197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Intelligent Transportation Systems Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1252345260","display_name":null,"funder_award_id":"61925302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4682923207","display_name":null,"funder_award_id":"62103033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1982752083","https://openalex.org/W2011742363","https://openalex.org/W2012076162","https://openalex.org/W2028501442","https://openalex.org/W2039133703","https://openalex.org/W2064675550","https://openalex.org/W2130449222","https://openalex.org/W2140115721","https://openalex.org/W2261059368","https://openalex.org/W2295598076","https://openalex.org/W2618530766","https://openalex.org/W2618782758","https://openalex.org/W2760535917","https://openalex.org/W2770173563","https://openalex.org/W2775795276","https://openalex.org/W2887126012","https://openalex.org/W2911964244","https://openalex.org/W2912985636","https://openalex.org/W2962874036","https://openalex.org/W2963108767","https://openalex.org/W2973962564","https://openalex.org/W2991109280","https://openalex.org/W2995012791","https://openalex.org/W2995727660","https://openalex.org/W2997073788","https://openalex.org/W3044811479","https://openalex.org/W3047806952","https://openalex.org/W3165137932","https://openalex.org/W3169906434","https://openalex.org/W3170039947","https://openalex.org/W3171984561","https://openalex.org/W4210389966","https://openalex.org/W4220694402","https://openalex.org/W4237613712","https://openalex.org/W4285233815","https://openalex.org/W4288335398","https://openalex.org/W4292415833","https://openalex.org/W4296714551","https://openalex.org/W4316661016","https://openalex.org/W6746638498","https://openalex.org/W6747218270","https://openalex.org/W6763136721"],"related_works":["https://openalex.org/W160673860","https://openalex.org/W2963642612","https://openalex.org/W4289548080","https://openalex.org/W1585680390","https://openalex.org/W3173587717","https://openalex.org/W4285322112","https://openalex.org/W3165456129","https://openalex.org/W2113005879","https://openalex.org/W4292794239","https://openalex.org/W4288350785"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"delays":[2],"for":[3,88,141],"high-speed":[4],"railways":[5],"(HSRs)":[6],"is":[7,60],"a":[8,43],"challenging":[9],"yet":[10],"significant":[11],"task.":[12],"The":[13,134],"historical":[14],"operation":[15,70],"data":[16,46,65,71,76,87,102],"of":[17,130,154],"the":[18,25,54,57,63,68,74,79,85,104,110,116,128,131,150],"HSRs,":[19],"implicating":[20],"delay":[21,48,75],"derivation":[22],"rules":[23],"under":[24,37,119],"dispatchers\u2019":[26],"rescheduling":[27,144],"strategies,":[28],"have":[29],"sparsity":[30],"characteristics,":[31],"resulting":[32],"in":[33],"heterogeneous":[34],"prediction":[35,49,93,117],"performances":[36],"different":[38,89,120,123],"scenarios.":[39],"This":[40],"article":[41],"proposes":[42],"Gaussian":[44,58],"noise":[45,59],"augmentation-based":[47],"method":[50,112],"to":[51,62],"cope":[52],"with":[53,122],"sparsity.":[55],"Specifically,":[56],"added":[61],"original":[64],"based":[66,97],"on":[67,98],"train":[69,143],"characteristics.":[72],"Then,":[73],"rather":[77],"than":[78],"full-state":[80],"dataset":[81],"are":[82],"selected":[83],"as":[84],"training":[86],"designed":[90],"machine":[91,124],"learning":[92,125],"models.":[94],"Numerous":[95],"studies":[96],"real":[99],"HSR":[100],"operational":[101],"from":[103],"Beijing":[105],"Railway":[106],"Bureau":[107],"show":[108],"that":[109],"proposed":[111],"could":[113,137],"significantly":[114,139],"improve":[115],"accuracy":[118],"scenarios":[121],"models,":[126],"verifying":[127],"effectiveness":[129],"performance":[132],"improvement.":[133],"relevant":[135],"results":[136],"be":[138],"helpful":[140],"real-time":[142],"and":[145],"passenger":[146],"management,":[147],"thus":[148],"improving":[149],"emergency":[151],"response":[152],"capabilities":[153],"HSRs.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}