{"id":"https://openalex.org/W4399601381","doi":"https://doi.org/10.1109/tits.2024.3409754","title":"Bayesian Spatio-Temporal Graph Convolutional Network for Railway Train Delay Prediction","display_name":"Bayesian Spatio-Temporal Graph Convolutional Network for Railway Train Delay Prediction","publication_year":2024,"publication_date":"2024-06-13","ids":{"openalex":"https://openalex.org/W4399601381","doi":"https://doi.org/10.1109/tits.2024.3409754"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3409754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3409754","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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/A5100372727","display_name":"Jianmin Li","orcid":"https://orcid.org/0000-0001-5324-2829"},"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":"Jianmin Li","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5324-2829","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101489170","display_name":"Xinyue Xu","orcid":"https://orcid.org/0000-0002-6878-4759"},"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":"Xinyue Xu","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6878-4759","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086343554","display_name":"Xin Ding","orcid":"https://orcid.org/0000-0001-5405-7414"},"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":"Xin Ding","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5405-7414","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014627828","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0003-3242-3182"},"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":"Jun Liu","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3242-3182","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060394098","display_name":"Bin Ran","orcid":"https://orcid.org/0000-0002-5464-0930"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Ran","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4655,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.9291024,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"25","issue":"7","first_page":"8193","last_page":"8208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9976999759674072,"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":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.647113561630249},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5764637589454651},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5658641457557678},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5143722295761108},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.45590418577194214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43001994490623474},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.415761262178421},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.30283868312835693},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20342615246772766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.647113561630249},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5764637589454651},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5658641457557678},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5143722295761108},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.45590418577194214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43001994490623474},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.415761262178421},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.30283868312835693},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20342615246772766}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3409754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3409754","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3337380680","display_name":null,"funder_award_id":"2022JBZY022","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"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":50,"referenced_works":["https://openalex.org/W573695890","https://openalex.org/W636878885","https://openalex.org/W824790654","https://openalex.org/W1964290466","https://openalex.org/W1985387745","https://openalex.org/W1988604380","https://openalex.org/W1995459276","https://openalex.org/W2044347817","https://openalex.org/W2053811733","https://openalex.org/W2079246731","https://openalex.org/W2097717114","https://openalex.org/W2117284974","https://openalex.org/W2491604858","https://openalex.org/W2610323442","https://openalex.org/W2615062698","https://openalex.org/W2618782758","https://openalex.org/W2742989960","https://openalex.org/W2751490822","https://openalex.org/W2791079235","https://openalex.org/W2801522993","https://openalex.org/W2887126012","https://openalex.org/W2901504064","https://openalex.org/W2924937155","https://openalex.org/W2936650651","https://openalex.org/W2945277272","https://openalex.org/W2957585919","https://openalex.org/W2973546771","https://openalex.org/W2994808218","https://openalex.org/W2997073788","https://openalex.org/W3008790764","https://openalex.org/W3044811479","https://openalex.org/W3080329174","https://openalex.org/W3114503169","https://openalex.org/W3119688269","https://openalex.org/W3145694833","https://openalex.org/W3165137932","https://openalex.org/W3169906434","https://openalex.org/W3187933708","https://openalex.org/W3189632276","https://openalex.org/W4200148354","https://openalex.org/W4206338598","https://openalex.org/W4214820798","https://openalex.org/W4220694402","https://openalex.org/W4224293834","https://openalex.org/W4225696831","https://openalex.org/W4234251942","https://openalex.org/W4312203572","https://openalex.org/W4388572495","https://openalex.org/W4392337929","https://openalex.org/W6616330077"],"related_works":["https://openalex.org/W2383034311","https://openalex.org/W2592745513","https://openalex.org/W643788828","https://openalex.org/W2391701421","https://openalex.org/W201565394","https://openalex.org/W2353852789","https://openalex.org/W4309448762","https://openalex.org/W1700460858","https://openalex.org/W4211221765","https://openalex.org/W2417824324"],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"a":[3,67,166,182],"novel":[4],"approach":[5],"that":[6,104,136],"integrates":[7],"dynamic":[8,33,60,68,71,93,108,154],"Bayesian":[9,69],"network":[10,17,103],"with":[11,145],"attention":[12,98],"based":[13,99],"spatio-temporal":[14,100,109],"graph":[15,101],"convolutional":[16,102],"to":[18,81,181],"forecast":[19],"railway":[20],"train":[21,29,36,39,75,85,112,158,174],"delays,":[22],"capturing":[23],"the":[24,32,45,55,84,107,116,130,137,141,146,149,157,197,200],"intricate":[25],"operation":[26],"interactions":[27],"between":[28],"events":[30,113],"and":[31,49,78,114,163,177,185],"evolution":[34],"of":[35,118,148,156,199],"delays.":[37],"Initially,":[38],"delay":[40,63,76,86,92,119,159,175],"patterns":[41,176],"are":[42],"identified":[43],"using":[44,126],"K-means":[46],"clustering":[47],"algorithm":[48],"incorporated":[50],"as":[51],"additional":[52],"variables":[53,179],"into":[54],"prediction":[56,150],"model.":[57,202],"To":[58],"capture":[59],"causality":[61,72,155],"in":[62,165,169,188],"propagation,":[64,94],"we":[65,95],"utilize":[66],"network-based":[70],"graph,":[73],"incorporating":[74],"data":[77,128],"domain":[79],"knowledge":[80],"effectively":[82,105],"model":[83,139],"propagation.":[87],"Leveraging":[88],"these":[89],"insights":[90],"on":[91],"propose":[96],"an":[97],"models":[106],"dependency":[110],"among":[111],"enhances":[115,161],"accuracy":[117],"predictions.":[120],"The":[121,152,192],"proposed":[122,138,201],"method":[123],"is":[124],"assessed":[125],"operational":[127],"from":[129],"Wuhan-Guangzhou":[131],"high-speed":[132],"railway.":[133],"Results":[134],"show":[135],"outperforms":[140],"baseline":[142],"models,":[143],"particularly":[144],"expansion":[147],"horizon.":[151],"learned":[153],"propagation":[160],"interpretability":[162],"results":[164],"6.97%":[167],"reduction":[168,187],"mean":[170,189],"absolute":[171,190],"error.":[172,191],"Furthermore,":[173],"weather":[178],"contribute":[180],"respective":[183],"12.85%":[184],"4.37%":[186],"statistical":[193],"tests":[194],"further":[195],"validate":[196],"efficacy":[198]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
