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This paper introduces a novel Frequency-Domain Multi-Modality modeling approach (FreMo) that addresses the challenges of forecasting in urban transportation systems by leveraging the distinct spectral characteristics of different transportation modes. By employing a Modality-Wise Frequency Filter (MFF) and a Frequency-Guided Synergy Integrator (FSI), FreMo enhances cross-modality synergy while minimizing noise and negative transfer. Experimental results demonstrate that FreMo outperforms existing state-of-the-art methods across various forecasting scenarios, highlighting its effectiveness and adaptability in real-world applications.
FreMo reveals that effective multi-modality transportation forecasting hinges on exploiting frequency-domain characteristics rather than traditional time-domain methods.
Multi-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 Multi-Modality modeling (FreMo) that explicitly exploits the frequency domain to enable adaptive and selective cross-modality synergy. FreMo disentangles modality-wise spectral refinement from cross-modality 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.
