{"id":"https://openalex.org/W4413349899","doi":"https://doi.org/10.1109/icde65448.2025.00220","title":"CrossST: An Efficient Pre-Training Framework for Cross-District Pattern Generalization in Urban Spatio-Temporal Forecasting","display_name":"CrossST: An Efficient Pre-Training Framework for Cross-District Pattern Generalization in Urban Spatio-Temporal Forecasting","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413349899","doi":"https://doi.org/10.1109/icde65448.2025.00220"},"language":"en","primary_location":{"id":"doi:10.1109/icde65448.2025.00220","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00220","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062271687","display_name":"Aoyu Liu","orcid":"https://orcid.org/0000-0002-8583-3989"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aoyu Liu","raw_affiliation_strings":["Ministry of Education, Tongji University,Key Laboratory of Embedded System and Service Computing,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ministry of Education, Tongji University,Key Laboratory of Embedded System and Service Computing,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048067916","display_name":"Yaying Zhang","orcid":"https://orcid.org/0000-0001-9552-3274"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaying Zhang","raw_affiliation_strings":["Ministry of Education, Tongji University,Key Laboratory of Embedded System and Service Computing,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ministry of Education, Tongji University,Key Laboratory of Embedded System and Service Computing,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":4.7709,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92811803,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2935","last_page":"2948"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.9179999828338623,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.9179999828338623,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9164999723434448,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9143999814987183,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.8243511915206909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7013596296310425},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5691699385643005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5654679536819458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4334612488746643},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14374199509620667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12419569492340088},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.05039703845977783}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8243511915206909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013596296310425},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5691699385643005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5654679536819458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4334612488746643},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14374199509620667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12419569492340088},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.05039703845977783},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde65448.2025.00220","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00220","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G48599867","display_name":null,"funder_award_id":"2024-6-ZD-02","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G5342982222","display_name":null,"funder_award_id":"2022YF-B4501704","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G818768642","display_name":null,"funder_award_id":"72342026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2407386500","https://openalex.org/W2756203131","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W3080253043","https://openalex.org/W3088611441","https://openalex.org/W3126367810","https://openalex.org/W3158304688","https://openalex.org/W3171958173","https://openalex.org/W3185341429","https://openalex.org/W4225341287","https://openalex.org/W4231449374","https://openalex.org/W4283315029","https://openalex.org/W4290927794","https://openalex.org/W4380480573","https://openalex.org/W4382239616","https://openalex.org/W4382246105","https://openalex.org/W4382449675","https://openalex.org/W4385270240","https://openalex.org/W4387717396","https://openalex.org/W4387846860","https://openalex.org/W4387848913","https://openalex.org/W4388936714","https://openalex.org/W4391987728","https://openalex.org/W4400909784","https://openalex.org/W4401023520","https://openalex.org/W4401024533","https://openalex.org/W4401857089","https://openalex.org/W4401863317","https://openalex.org/W4401864135","https://openalex.org/W4403182907","https://openalex.org/W4406368131","https://openalex.org/W4409363714"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","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":{"Urban":[0],"spatio-temporal":[1,31,42,70,83,125,133],"forecasting":[2],"is":[3],"critical":[4],"for":[5,23],"modern":[6],"urban":[7,30],"governance,":[8],"especially":[9],"in":[10,20,29,92],"traffic":[11],"management,":[12],"resource":[13],"planning,":[14],"and":[15,38,87,102,107,143,153,161,186],"emergency":[16],"response.":[17],"Despite":[18],"advancements":[19],"pre-trained":[21],"models":[22],"natural":[24],"language":[25],"processing,":[26],"challenges":[27],"persist":[28],"forecasting.":[32],"Existing":[33],"methods":[34],"struggle":[35],"to":[36,67,85,116,140],"identify":[37,117],"generalize":[39],"universal":[40,69,129],"cross-district":[41,74,146],"patterns,":[43],"while":[44,111,179],"computational":[45,165,182],"limitations":[46],"hinder":[47],"the":[48],"extraction":[49],"of":[50],"complex":[51],"patterns":[52,71,91,130,134],"from":[53,131],"large-scale":[54,82],"data.":[55],"In":[56],"this":[57],"study,":[58],"we":[59],"propose":[60],"CrossST,":[61],"an":[62],"efficient":[63,145],"pre-training":[64,79],"framework":[65],"designed":[66],"capture":[68],"across":[72],"large-scale,":[73],"scenarios.":[75],"Specifically,":[76],"CrossST":[77,171],"performs":[78],"on":[80],"various":[81],"datasets":[84,185],"learn":[86],"store":[88],"diverse":[89,132],"valuable":[90],"its":[93],"pattern":[94],"bank.":[95],"It":[96],"captures":[97],"temporal":[98,150],"dependencies,":[99],"including":[100],"periodicity":[101],"trends,":[103],"through":[104],"frequency":[105],"domain":[106,109],"time":[108],"analysis,":[110],"leveraging":[112],"graph":[113],"attention":[114],"mechanisms":[115],"dynamic":[118],"spatial":[119,154],"propagation":[120],"patterns.":[121],"During":[122],"fine-tuning,":[123],"a":[124],"disentanglement":[126],"strategy":[127],"separates":[128],"stored":[135],"during":[136],"pre-training,":[137],"improving":[138,175],"generalization":[139,178],"downstream":[141,176],"tasks":[142],"enabling":[144],"knowledge":[147],"transfer.":[148],"Additionally,":[149],"information":[151],"aggregation":[152],"linear":[155],"optimization":[156],"strategies":[157],"enhance":[158],"CrossST's":[159],"efficiency":[160],"scalability,":[162],"significantly":[163],"reducing":[164],"costs.":[166],"Extensive":[167],"experiments":[168],"demonstrate":[169],"that":[170],"outperforms":[172],"state-of-the-art":[173],"baselines,":[174],"task":[177],"maintaining":[180],"low":[181],"overhead.":[183],"The":[184],"code":[187],"are":[188],"available":[189],"at":[190],"https://github.com/Aoyu-Liu/CrossST.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}