{"id":"https://openalex.org/W7137950940","doi":"https://doi.org/10.1609/aaai.v40i25.39239","title":"Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach","display_name":"Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137950940","doi":"https://doi.org/10.1609/aaai.v40i25.39239"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i25.39239","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39239","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39239/43200","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39239/43200","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099069904","display_name":"Cl\u00e9ment Elliker","orcid":"https://orcid.org/0009-0001-9795-0285"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210139461","display_name":"Laboratoire d'Informatique de l'\u00c9cole Polytechnique","ror":"https://ror.org/04afed728","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I142476485","https://openalex.org/I4210139461","https://openalex.org/I4210145102","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Cl\u00e9ment Elliker","raw_affiliation_strings":["LIX (\u00c9cole Polytechnique, IP Paris, CNRS)"],"affiliations":[{"raw_affiliation_string":"LIX (\u00c9cole Polytechnique, IP Paris, CNRS)","institution_ids":["https://openalex.org/I4210139461","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121267860","display_name":"Jesse Read","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210139461","display_name":"Laboratoire d'Informatique de l'\u00c9cole Polytechnique","ror":"https://ror.org/04afed728","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I142476485","https://openalex.org/I4210139461","https://openalex.org/I4210145102","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jesse Read","raw_affiliation_strings":["LIX (\u00c9cole Polytechnique, IP Paris, CNRS)"],"affiliations":[{"raw_affiliation_string":"LIX (\u00c9cole Polytechnique, IP Paris, CNRS)","institution_ids":["https://openalex.org/I4210139461","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121274282","display_name":"Sonia Vanier","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210139461","display_name":"Laboratoire d'Informatique de l'\u00c9cole Polytechnique","ror":"https://ror.org/04afed728","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I142476485","https://openalex.org/I4210139461","https://openalex.org/I4210145102","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Sonia Vanier","raw_affiliation_strings":["LIX (\u00c9cole Polytechnique, IP Paris, CNRS)"],"affiliations":[{"raw_affiliation_string":"LIX (\u00c9cole Polytechnique, IP Paris, CNRS)","institution_ids":["https://openalex.org/I4210139461","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121270186","display_name":"Albert Bifet","orcid":null},"institutions":[{"id":"https://openalex.org/I12356871","display_name":"T\u00e9l\u00e9com Paris","ror":"https://ror.org/01naq7912","country_code":"FR","type":"education","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I4210165912","display_name":"Laboratoire Traitement et Communication de l\u2019Information","ror":"https://ror.org/057er4c39","country_code":"FR","type":"facility","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4210165912"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Albert Bifet","raw_affiliation_strings":["AI Institute, University of Waikato\nLTCI, T\u00e9l\u00e9com Paris, IP Paris"],"affiliations":[{"raw_affiliation_string":"AI Institute, University of Waikato\nLTCI, T\u00e9l\u00e9com Paris, IP Paris","institution_ids":["https://openalex.org/I4210165912","https://openalex.org/I12356871"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5099069904"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210139461"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"25","first_page":"20977","last_page":"20984"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.8309000134468079,"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":0.8309000134468079,"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":0.05420000106096268,"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.02239999920129776,"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/fidelity","display_name":"Fidelity","score":0.572700023651123},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.552299976348877},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.48750001192092896},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.39809998869895935},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.34130001068115234},{"id":"https://openalex.org/keywords/continuation","display_name":"Continuation","score":0.3411000072956085},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.33329999446868896},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.30160000920295715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6953999996185303},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.572700023651123},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.552299976348877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5291000008583069},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.48750001192092896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4422000050544739},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C78639753","wikidata":"https://www.wikidata.org/wiki/Q3318160","display_name":"Behavioral modeling","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.26409998536109924}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i25.39239","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39239","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39239/43200","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i25.39239","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39239","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39239/43200","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6480225324630737,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7137950940.pdf","grobid_xml":"https://content.openalex.org/works/W7137950940.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"prediction":[1],"of":[2,13,75,82,128,151],"train":[3,112],"delays":[4],"is":[5],"essential":[6],"for":[7],"enhancing":[8],"the":[9,72,79,102,146],"robustness":[10],"and":[11,134,148],"efficiency":[12],"railway":[14,104],"transportation":[15],"systems.":[16],"In":[17],"this":[18],"work,":[19],"we":[20],"reframe":[21],"delay":[22,152],"forecasting":[23,87],"as":[24],"a":[25,41,96],"stochastic":[26],"simulation":[27],"task,":[28],"modeling":[29],"state-transition":[30],"dynamics":[31],"through":[32],"imitation":[33],"learning.":[34],"We":[35,92],"introduce":[36],"Drift-Corrected":[37],"Imitation":[38],"Learning":[39],"(DCIL),":[40],"novel":[42],"self-supervised":[43],"algorithm":[44],"that":[45],"extends":[46],"DAgger":[47],"by":[48],"incorporating":[49],"distance-based":[50],"drift":[51],"correction,":[52],"thereby":[53],"mitigating":[54],"covariate":[55],"shift":[56],"during":[57],"rollouts":[58],"without":[59],"requiring":[60],"access":[61],"to":[62,120],"an":[63],"external":[64],"oracle":[65],"or":[66],"adversarial":[67],"schemes.":[68],"Our":[69,114],"approach":[70],"synthesizes":[71],"dynamical":[73],"fidelity":[74],"event-driven":[76],"models":[77,133],"with":[78],"representational":[80],"capacity":[81],"data-driven":[83],"methods,":[84],"enabling":[85],"uncertainty-aware":[86],"via":[88],"Monte":[89],"Carlo":[90],"simulation.":[91],"evaluate":[93],"DCIL":[94,129],"using":[95],"comprehensive":[97],"real-world":[98],"dataset":[99],"from":[100],"\\textsc{Infrabel},":[101],"Belgian":[103],"infrastructure":[105],"manager,":[106],"which":[107],"encompasses":[108],"over":[109,130],"three":[110],"million":[111],"movements.":[113],"results,":[115],"focused":[116],"on":[117,137],"predictions":[118],"up":[119],"30":[121],"minutes":[122],"ahead,":[123],"demonstrate":[124],"superior":[125],"predictive":[126],"performance":[127],"traditional":[131],"regression":[132],"behavioral":[135],"cloning":[136],"deep":[138],"learning":[139],"architectures,":[140],"highlighting":[141],"its":[142],"effectiveness":[143],"in":[144,154],"capturing":[145],"sequential":[147],"uncertain":[149],"nature":[150],"propagation":[153],"large-scale":[155],"networks.":[156]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}
