{"id":"https://openalex.org/W1993698166","doi":"https://doi.org/10.4304/jsw.9.5.1237-1244","title":"Analysis on Train Stopping Accuracy based on Regression Algorithms","display_name":"Analysis on Train Stopping Accuracy based on Regression Algorithms","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W1993698166","doi":"https://doi.org/10.4304/jsw.9.5.1237-1244","mag":"1993698166"},"language":"en","primary_location":{"id":"doi:10.4304/jsw.9.5.1237-1244","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jsw.9.5.1237-1244","pdf_url":null,"source":{"id":"https://openalex.org/S114141714","display_name":"Journal of Software","issn_l":"1796-217X","issn":["1796-217X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Software","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/A5018662596","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0003-1684-9714"},"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":"Lin Ma","raw_affiliation_strings":["National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, 100044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, 100044","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101695668","display_name":"Xiangyu Zeng","orcid":"https://orcid.org/0000-0002-9614-4230"},"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":"Xiangyu Zeng","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"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":0.15520899,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9937000274658203,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9914000034332275,"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":0.9100253582000732},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4897952079772949},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46328204870224},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44766801595687866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3825761377811432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34044450521469116},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15721821784973145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.05574628710746765}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9100253582000732},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4897952079772949},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46328204870224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44766801595687866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3825761377811432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34044450521469116},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15721821784973145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.05574628710746765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4304/jsw.9.5.1237-1244","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jsw.9.5.1237-1244","pdf_url":null,"source":{"id":"https://openalex.org/S114141714","display_name":"Journal of Software","issn_l":"1796-217X","issn":["1796-217X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Software","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1570448133","https://openalex.org/W1875851731","https://openalex.org/W1988864670","https://openalex.org/W1993140370","https://openalex.org/W1994730098","https://openalex.org/W1997364324","https://openalex.org/W2014725748","https://openalex.org/W2044936056","https://openalex.org/W2047028564","https://openalex.org/W2051937592","https://openalex.org/W2066796151","https://openalex.org/W2067741897","https://openalex.org/W2075490785","https://openalex.org/W2095012728","https://openalex.org/W2106008437","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2140190241","https://openalex.org/W2167293542","https://openalex.org/W2364331408","https://openalex.org/W4234698323","https://openalex.org/W6634094483","https://openalex.org/W6648975594","https://openalex.org/W6680704940"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3209574120","https://openalex.org/W3107602296","https://openalex.org/W4312192474","https://openalex.org/W4210805261","https://openalex.org/W4387297750"],"abstract_inverted_index":{"Stopping":[0],"accuracy":[1,45,136,175],"is":[2],"one":[3],"of":[4,9,11,40],"the":[5,37,73,86,105,108,112,116,133,157,162],"most":[6],"important":[7],"indexes":[8],"efficiency":[10],"automatic":[12],"train":[13,74,163],"operation":[14],"(ATO)":[15],"systems.":[16,178],"Traditional":[17],"stopping":[18,44,53,59,75,87,113,174],"control":[19],"algorithms":[20],"in":[21],"ATO":[22,177],"systems":[23],"have":[24,30,83],"some":[25],"drawbacks,":[26],"as":[27],"many":[28,48],"factors":[29,49,81,110],"not":[31],"been":[32],"taken":[33],"into":[34],"account.":[35],"In":[36,61],"large":[38],"amount":[39],"field-collected":[41],"data":[42,66],"about":[43],"there":[46],"are":[47,69,98,119],"(e.g.":[50],"system":[51],"delays,":[52],"time,":[54],"net":[55,96,144],"pressure)":[56],"which":[57,82,131,159,167],"affecting":[58],"accuracy.":[60,76,88,114],"this":[62],"paper,":[63],"three":[64,117],"popular":[65],"mining":[67],"methods":[68],"proposed":[70],"to":[71,100,103,172],"analyze":[72],"Firstly,":[77],"we":[78,155],"find":[79],"fifteen":[80,109],"impact":[84],"on":[85,151],"Then,":[89,115],"ridge":[90],"regression,":[91],"lasso":[92],"regression":[93,97,145],"and":[94,111,137],"elastic":[95,143],"employed":[99],"mine":[101],"models":[102,118],"reflecting":[104],"relationship":[106],"between":[107,135],"compared":[120],"by":[121],"using":[122],"Akaike":[123],"information":[124],"criterion":[125,130],"(AIC),":[126],"a":[127,148,170],"model":[128,146],"selection":[129],"considering":[132],"trade-off":[134],"complexity.\u00a0The":[138],"computational":[139],"results":[140],"show":[141],"that":[142],"has":[147],"best":[149],"performance":[150],"AIC":[152],"value.":[153],"Finally,":[154],"obtain":[156],"parameters":[158],"can":[160,168],"make":[161],"stop":[164],"more":[165],"accurately":[166],"provide":[169],"reference":[171],"improve":[173],"for":[176]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
