[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83392-en":3,"doc-seo-83392-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83392,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Frequency-Domain Multi-Modality Transportation Modeling","Multi-modality transportation models urban systems where coupled modes (e.g., traffic flow and public transit) share temporal patterns. Accurate forecasting remains difficult because modalities have distinct spectral characteristics and interact unevenly across frequencies, while prior approaches largely work in the time domain or use coarse fusion. FreMo (Frequency-Domain Multi-Modality modeling) leverages frequency-domain structure with a Modality-Wise Frequency Filter for adaptive spectral refinement and a Frequency-Guided Synergy Integrator for selective cross-modality aggregation. Experiments on real-world datasets show consistent gains and strong generalization.","Frequency-Domain Multi-Modality Transportation Modeling  \nJiewen Deng  \n[dengjw1@outlook.com](dengjw1@outlook.com)[ ](dengjw1@outlook.com)Southern University of Science and Technology Shenzhen, China  \nHangchen Liu  \n[liuhc3@outlook.com](liuhc3@outlook.com)[ ](liuhc3@outlook.com)The University of Tokyo Tokyo, Japan  \nJunchen Li  \n[bobjunchen2@gmail.com](bobjunchen2@gmail.com)[ ](bobjunchen2@gmail.com)Southern University of Science and Technology Shenzhen, China  \narXiv :2607 .08475v 1 [ cs .LG] 9 Jul 2026  \nBoyuan Zhang  \n[zby9973@outlook.com](zby9973@outlook.com)[ ](zby9973@outlook.com)The University of Tokyo Tokyo, Japan  \nAbstract  \nMulti-modality transportation refers to urban systems composed of multiple transportation modes, such as traffic flow and public transit, whose dynamics are coupled by shared temporal patterns. Accurate multi-modality transportation forecasting remains challenging because (1) different modalities exhibit distinct spectral characteristics and (2) interact unevenly across frequencies, whereas most existing methods operate primarily in the time domain or rely on coarse feature fusion. To address these limitations, we propose a lightweight yet effective Frequency-Domain MultiModality modeling (FreMo) that explicitly exploits the frequency domain to enable adaptive and selective cross-modality synergy. FreMo disentangles modality-wise spectral refinement from crossmodality synergy and supports plug-and-play integration with general time series backbones. Specifically, FreMo introduces a Modality-Wise Frequency Filter (MFF) to adaptively refine spectral components within each modality, emphasizing informative frequencies while suppressing noise. FreMo further incorporates a Frequency-Guided Synergy Integrator (FSI) that selectively aggregates information across modalities based on their relative contribution at each frequency, facilitating effective cross-modality knowledge sharing while mitigating negative transfer. Extensive experiments on real-world datasets show that FreMo consistently outperforms state-of-the-art baselines, with superior performance and generalization across diverse forecasting scenarios. The code is available at [https://github.com/beginner-sketch/FreMo](https://github.com/beginner-sketch/FreMo).  \nCCS Concepts  \n• Information systems → Spatial-temporal systems; • Computing methodologies → Artificial intelligence.  \nKeywords  \nfrequency-domain, multi-modality transportation modeling  \n∗ Corresponding author.  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. KDD’26, Jeju Island, Republic of Korea  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2259-2/2026/08  \n[https://doi.org/10.1145/3770855.3818022](https://doi.org/10.1145/3770855.3818022)  \nRenhe Jiang∗ [jiangrh@csis.u-tokyo.ac.jp](jiangrh@csis.u-tokyo.ac.jp)[ ](jiangrh@csis.u-tokyo.ac.jp)The University of Tokyo  \nTokyo, Japan  \nACM Reference Format:  \nJiewen Deng, Hangchen Liu, Junchen Li, Boyuan Zhang, and Renhe Jiang.  \n2026. Frequency-Domain Multi-Modality Transportation Modeling. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD’26), August 09–13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. [https://doi.org/10.1145/3770855.3818022](https://doi.org/10.1145/3770855.3818022)  \nTime (a) Temporal patterns  \n\n|  |  |  |  |  |  | \u003Cbr> |\n| --- | --- | --- | --- | --- | --- | --- |\n\nFrequency (Low → High)  \n(b) Frequency spectra  \nFrequency (Low → High)  \n(c) Spectral coherence between Bike and Taxi Outflow  \n1.0  \n0.5  \n0.0  \nCoherence  \nFigure 1: Multi-modality transportation data with representative modalities (Bike and Taxi Outflow). (a) Temporal patterns exhibit aligned trends but mismatched details. (b) Frequency spectra differ across modalities. (c) Spectral coherence is high at low frequencies but drops at high frequencies.  \n1 Introduction  \nMulti-modality transportation data are structu","cbCait2Er0QASzZY","https://ap.wps.com/l/cbCait2Er0QASzZY","pdf",1392946,3,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does FreMo address in multi-modality transportation forecasting?\",\"answer\":\"FreMo targets the challenge of capturing shared patterns across transport modalities while preserving modality-specific traits, especially when cross-modality interactions vary across frequencies.\"},{\"question\":\"How does FreMo improve modeling compared with time-domain or coarse fusion methods?\",\"answer\":\"FreMo explicitly operates in the frequency domain, separating modality-wise spectral refinement from cross-modality synergy and aggregating information selectively according to each frequency’s contribution.\"},{\"question\":\"What are the main components of FreMo?\",\"answer\":\"FreMo uses a Modality-Wise Frequency Filter (MFF) to emphasize informative spectral components within each modality and a Frequency-Guided Synergy Integrator (FSI) to selectively combine information across modalities while reducing negative transfer.\"}]",1784187197,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"frequency-domain-multi-modality-transportation-modeling","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/frequency-domain-multi-modality-transportation-modeling/83392/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does FreMo address in multi-modality transportation forecasting?","Question",{"text":75,"@type":76},"FreMo targets the challenge of capturing shared patterns across transport modalities while preserving modality-specific traits, especially when cross-modality interactions vary across frequencies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does FreMo improve modeling compared with time-domain or coarse fusion methods?",{"text":80,"@type":76},"FreMo explicitly operates in the frequency domain, separating modality-wise spectral refinement from cross-modality synergy and aggregating information selectively according to each frequency’s contribution.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main components of FreMo?",{"text":84,"@type":76},"FreMo uses a Modality-Wise Frequency Filter (MFF) to emphasize informative spectral components within each modality and a Frequency-Guided Synergy Integrator (FSI) to selectively combine information across 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