{"id":"https://openalex.org/W4404038645","doi":"https://doi.org/10.1145/3681778.3698784","title":"Rail transit delay forecasting with Causal Machine Learning","display_name":"Rail transit delay forecasting with Causal Machine Learning","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4404038645","doi":"https://doi.org/10.1145/3681778.3698784"},"language":"en","primary_location":{"id":"doi:10.1145/3681778.3698784","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681778.3698784","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3681778.3698784","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3681778.3698784","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004948335","display_name":"Nishtha Srivastava","orcid":"https://orcid.org/0000-0002-9372-394X"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nishtha Srivastava","raw_affiliation_strings":["Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India"],"raw_orcid":"https://orcid.org/0000-0002-9372-394X","affiliations":[{"raw_affiliation_string":"Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070093209","display_name":"Bhavesh N. Gohil","orcid":"https://orcid.org/0000-0002-9407-4259"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhavesh N. Gohil","raw_affiliation_strings":["Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India"],"raw_orcid":"https://orcid.org/0000-0002-9407-4259","affiliations":[{"raw_affiliation_string":"Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009118014","display_name":"Suprio Ray","orcid":"https://orcid.org/0000-0003-0681-9685"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Suprio Ray","raw_affiliation_strings":["University of New Brunswick, Fredericton, Canada"],"raw_orcid":"https://orcid.org/0000-0003-0681-9685","affiliations":[{"raw_affiliation_string":"University of New Brunswick, Fredericton, Canada","institution_ids":["https://openalex.org/I106938459"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6962072849273682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4851142168045044},{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.4621347486972809},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44125914573669434},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.20435026288032532},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17168331146240234},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.1659528911113739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6962072849273682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4851142168045044},{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.4621347486972809},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44125914573669434},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.20435026288032532},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17168331146240234},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.1659528911113739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3681778.3698784","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681778.3698784","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3681778.3698784","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3681778.3698784","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3681778.3698784","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3681778.3698784","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404038645.pdf"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1587383887","https://openalex.org/W1980254386","https://openalex.org/W1989265314","https://openalex.org/W1995459276","https://openalex.org/W2011485768","https://openalex.org/W2021304214","https://openalex.org/W2042580362","https://openalex.org/W2110435420","https://openalex.org/W2140899775","https://openalex.org/W2148714761","https://openalex.org/W2152456876","https://openalex.org/W2528353362","https://openalex.org/W2887126012","https://openalex.org/W2945277272","https://openalex.org/W2964669335","https://openalex.org/W2990908510","https://openalex.org/W4220694402","https://openalex.org/W4256361765","https://openalex.org/W4283764350","https://openalex.org/W4311486892","https://openalex.org/W4394964969"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,79],"rapid":[1],"evolution":[2],"of":[3,104],"public":[4,117],"transport":[5],"and":[6,27,56,71,86,94,108,116,132],"advances":[7],"in":[8],"analytics":[9],"have":[10],"significantly":[11],"transformed":[12],"the":[13,62,96],"way":[14],"we":[15,67],"enhance":[16,76],"transit":[17,20,54,106,130],"services.":[18],"Rail":[19],"systems,":[21],"celebrated":[22],"for":[23,112],"their":[24],"comfort,":[25],"speed,":[26],"minimal":[28],"environmental":[29],"impact,":[30],"face":[31],"ongoing":[32],"challenges":[33],"due":[34],"to":[35,51,75,92,128],"persistent":[36],"delays.":[37],"We":[38],"introduce":[39],"a":[40,101,135],"novel":[41],"approach":[42],"that":[43],"integrates":[44],"causal":[45,59,72,122],"inference":[46,73],"with":[47],"machine":[48],"learning":[49],"techniques":[50],"predict":[52],"rail":[53,105],"delays":[55,107],"uncover":[57],"key":[58],"factors.":[60],"Utilizing":[61],"New":[63],"Jersey":[64],"Transit":[65],"dataset,":[66],"apply":[68],"uplift":[69],"modeling":[70],"methods":[74],"delay":[77],"predictions.":[78,97],"study":[80],"employs":[81],"Individual":[82],"Treatment":[83,88],"Effect":[84,89],"(ITE)":[85],"Average":[87],"(ATE)":[90],"metrics":[91],"interpret":[93],"validate":[95],"Our":[98],"research":[99],"offers":[100],"comprehensive":[102],"understanding":[103],"provides":[109],"actionable":[110],"insights":[111],"policymakers,":[113],"urban":[114],"planners,":[115],"health":[118],"officials.":[119],"By":[120],"advancing":[121],"analytical":[123],"techniques,":[124],"this":[125],"work":[126],"aims":[127],"improve":[129],"reliability":[131],"efficiency":[133],"on":[134],"global":[136],"scale.":[137]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}