{"id":"https://openalex.org/W7164024779","doi":"https://doi.org/10.48550/arxiv.2606.07695","title":"DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems","display_name":"DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7164024779","doi":"https://doi.org/10.48550/arxiv.2606.07695"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059585326","display_name":"Yongchao Li","orcid":"https://orcid.org/0000-0001-8973-3013"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yongchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138221565","display_name":"Yang Li (7082)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138269940","display_name":"Zhuoxuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhuoxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138274564","display_name":"Jun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138229134","display_name":"Chu Zhang","orcid":"https://orcid.org/0009-0006-3272-2544"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138241076","display_name":"Jinde Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Jinde","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138210436","display_name":"Leszek Rutkowski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rutkowski, Leszek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9890000224113464,"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":0.9890000224113464,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.002400000113993883,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.0013000000035390258,"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/scalability","display_name":"Scalability","score":0.6572999954223633},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.4050000011920929},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.33489999175071716},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.28700000047683716},{"id":"https://openalex.org/keywords/complex-system","display_name":"Complex system","score":0.2799000144004822},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.27390000224113464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192000150680542},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4246000051498413},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34450000524520874},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C2777489503","wikidata":"https://www.wikidata.org/wiki/Q7698936","display_name":"Temporal scales","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8363958597183228}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-Modality":[0],"Spatio-Temporal":[1],"Forecasting":[2],"(MoSTF)":[3],"extends":[4],"traditional":[5],"spatio-temporal":[6,18],"forecasting":[7],"by":[8,153],"incorporating":[9],"diverse":[10],"traffic":[11,143],"modalities.":[12],"Despite":[13],"significant":[14],"recent":[15],"strides":[16],"in":[17,168],"modeling,":[19],"existing":[20,165],"approaches":[21],"often":[22],"fail":[23],"to":[24,76,124],"explicitly":[25,82],"model":[26,83],"the":[27,64,84,147],"coupling":[28],"relationships":[29,85],"between":[30,86],"different":[31],"modality":[32],"variables.":[33,87],"Accurate":[34],"MoSTF":[35],"is":[36],"challenging,":[37],"as":[38],"it":[39],"requires":[40],"modeling":[41,110],"(1)":[42],"temporal":[43,127],"dynamic":[44],"heterogeneity":[45],"under":[46,129],"exogenous":[47],"influences":[48],"and":[49,81,104,114,173],"(2)":[50],"heterogeneous":[51,78],"spatial":[52,79],"dependencies":[53,113],"alongside":[54],"complex":[55],"cross-variable":[56],"couplings.":[57,116],"To":[58],"address":[59],"these":[60,156],"challenges,":[61],"we":[62,118],"propose":[63],"Dual-Domain":[65],"Spectral":[66],"Filtering":[67],"Network":[68],"(DSFNet).":[69],"Our":[70],"framework":[71],"employs":[72],"dual-domain":[73],"spectral":[74,106],"filtering":[75],"capture":[77],"patterns":[80],"Unlike":[88],"graph-based":[89],"message":[90],"passing":[91],"or":[92],"dense":[93],"attention":[94],"over":[95],"node-modality":[96],"pairs,":[97],"DSFNet":[98,150,162],"factorizes":[99],"space-modality":[100],"interactions":[101],"into":[102],"feature-domain":[103],"spatial-domain":[105],"operators,":[107],"enabling":[108],"scalable":[109],"of":[111],"nonlocal":[112],"cross-modality":[115],"Furthermore,":[117],"introduce":[119],"an":[120],"external":[121,130],"gating":[122],"mechanism":[123],"adaptively":[125],"regulate":[126],"dynamics":[128],"influences.":[131],"We":[132],"validate":[133],"our":[134],"method":[135],"through":[136],"extensive":[137],"experiments":[138],"on":[139],"five":[140],"representative":[141],"real-world":[142],"datasets.":[144,157],"Compared":[145],"with":[146],"second-best":[148],"baselines,":[149],"reduces":[151],"MAE":[152],"3.21%-10.16%":[154],"across":[155],"The":[158],"results":[159],"demonstrate":[160],"that":[161],"significantly":[163],"outperforms":[164],"state-of-the-art":[166],"baselines":[167],"accuracy":[169],"while":[170],"exhibiting":[171],"efficiency":[172],"robustness.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
