{"id":"https://openalex.org/W4412482591","doi":"https://doi.org/10.1109/wfcs63373.2025.11077652","title":"A Gated-Guided Serial CNN-Transformer Network for High-Speed Railway Traffic Prediction","display_name":"A Gated-Guided Serial CNN-Transformer Network for High-Speed Railway Traffic Prediction","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4412482591","doi":"https://doi.org/10.1109/wfcs63373.2025.11077652"},"language":"en","primary_location":{"id":"doi:10.1109/wfcs63373.2025.11077652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wfcs63373.2025.11077652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Factory Communication Systems (WFCS)","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/A5054609554","display_name":"Haoyi Ma","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":"Haoyi Ma","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033845305","display_name":"Bo L\u00fc","orcid":"https://orcid.org/0000-0001-7953-7131"},"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":"Binjie Lu","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072070085","display_name":"Tao Zheng","orcid":"https://orcid.org/0000-0002-1677-6466"},"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":"Tao Zheng","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065495584","display_name":"Kyi Thar","orcid":null},"institutions":[{"id":"https://openalex.org/I56475706","display_name":"Mid Sweden University","ror":"https://ror.org/019k1pd13","country_code":"SE","type":"education","lineage":["https://openalex.org/I56475706"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Kyi Thar","raw_affiliation_strings":["Mid Sweden University,Department of Computer and Electrical Engineering,Sweden"],"affiliations":[{"raw_affiliation_string":"Mid Sweden University,Department of Computer and Electrical Engineering,Sweden","institution_ids":["https://openalex.org/I56475706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004012289","display_name":"Mikael Gidlund","orcid":"https://orcid.org/0000-0003-0873-7827"},"institutions":[{"id":"https://openalex.org/I56475706","display_name":"Mid Sweden University","ror":"https://ror.org/019k1pd13","country_code":"SE","type":"education","lineage":["https://openalex.org/I56475706"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Mikael Gidlund","raw_affiliation_strings":["Mid Sweden University,Department of Computer and Electrical Engineering,Sweden"],"affiliations":[{"raw_affiliation_string":"Mid Sweden University,Department of Computer and Electrical Engineering,Sweden","institution_ids":["https://openalex.org/I56475706"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054609554"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22649639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9972000122070312,"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":0.9972000122070312,"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/T10320","display_name":"Neural Networks and Applications","score":0.9825999736785889,"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/T14474","display_name":"Industrial Technology and Control Systems","score":0.9614999890327454,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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":0.698683500289917},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5757906436920166},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3397482633590698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3354477882385254},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.2530030310153961},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1940370798110962},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08674660325050354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.698683500289917},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5757906436920166},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3397482633590698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3354477882385254},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.2530030310153961},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1940370798110962},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08674660325050354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wfcs63373.2025.11077652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wfcs63373.2025.11077652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Factory Communication Systems (WFCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W848133049","https://openalex.org/W1946587052","https://openalex.org/W2048138820","https://openalex.org/W2284594984","https://openalex.org/W2485070318","https://openalex.org/W2898777543","https://openalex.org/W2949449669","https://openalex.org/W2954731415","https://openalex.org/W2964336507","https://openalex.org/W3017194848","https://openalex.org/W3107324520","https://openalex.org/W3177318507","https://openalex.org/W3180637339","https://openalex.org/W3212890323","https://openalex.org/W4205422274","https://openalex.org/W4220940433","https://openalex.org/W4224073594","https://openalex.org/W4284991965","https://openalex.org/W4288086393","https://openalex.org/W4294167343","https://openalex.org/W4295854510","https://openalex.org/W4312828436","https://openalex.org/W4367666062","https://openalex.org/W4385245566","https://openalex.org/W4389540888","https://openalex.org/W4390188632","https://openalex.org/W6764679822","https://openalex.org/W6797155008"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Accurate":[0],"traffic":[1,156],"forecasting":[2],"in":[3,40,128],"high-speed":[4],"railway":[5,155],"(HSR)":[6],"systems":[7],"is":[8],"hindered":[9],"by":[10,105],"abrupt":[11],"signal":[12],"fluctuations":[13],"and":[14,28,55,83,117,134],"varied":[15],"mobility":[16],"scenarios.":[17],"Conventional":[18],"approaches":[19],"that":[20,59,122],"rely":[21],"on":[22,96,114],"fixed":[23,110],"weighted":[24],"combinations":[25],"of":[26,92,142],"local":[27,76],"global":[29],"features":[30],"are":[31],"unable":[32],"to":[33,36,108,131],"adjust":[34],"rapidly":[35],"real-time":[37,154],"changes,":[38],"resulting":[39],"suboptimal":[41],"performance.":[42],"To":[43],"address":[44],"this":[45],"limitation,":[46],"we":[47],"propose":[48],"a":[49,61,67,125],"novel":[50],"gated":[51],"guided":[52],"serial":[53],"CNN":[54],"Transformer":[56],"network":[57],"(GsCT)":[58],"employs":[60],"dynamic":[62,100],"combination":[63],"mechanism":[64],"implemented":[65],"via":[66],"multilayer":[68],"perceptron":[69],"(MLP).":[70],"In":[71],"GsCT,":[72],"CNNs":[73],"capture":[74],"fine-grained":[75],"variations":[77],"while":[78],"Transformers":[79],"model":[80],"longrange":[81],"dependencies,":[82],"the":[84,90,135,143,159],"adaptive":[85],"MLP-based":[86],"gating":[87,137],"module":[88],"adjusts":[89],"contribution":[91],"each":[93],"branch":[94],"based":[95],"time-window":[97],"statistics.":[98],"This":[99],"fusion":[101,145],"improves":[102],"prediction":[103],"quality":[104],"6.5%":[106],"compared":[107],"conventional":[109],"weighting":[111],"mechanisms.":[112],"Evaluations":[113],"both":[115],"public":[116],"real-world":[118],"HSR":[119,163],"datasets":[120],"demonstrate":[121],"GsCT":[123,148],"achieves":[124],"2.4%":[126],"reduction":[127],"RMSE":[129],"relative":[130],"LSTM-based":[132],"methods,":[133],"learned":[136],"coefficients":[138],"offer":[139],"transparent":[140],"interpretability":[141],"feature":[144],"process.":[146],"Overall,":[147],"provides":[149],"an":[150],"effective":[151],"solution":[152],"for":[153,161],"forecasting,":[157],"paving":[158],"way":[160],"next-generation":[162],"services.":[164]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}