Frequency-Domain Multi-Modality Transportation Modeling

2026-07-09 · Source: Takara TLDR - Daily AI Papers · Field: Transportation & Mobility — Public Transportation & Urban Mobility, Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

FreMo, a lightweight Frequency-Domain Multi-Modality modeling approach, addresses challenges in urban multi-modality transportation forecasting. It tackles issues where different modalities, such as traffic flow and public transit, exhibit distinct spectral characteristics and interact unevenly across frequencies, a limitation of many time-domain methods. FreMo explicitly leverages the frequency domain to achieve adaptive and selective cross-modality synergy. It incorporates a Modality-Wise Frequency Filter (MFF) to refine spectral components and a Frequency-Guided Synergy Integrator (FSI) to selectively aggregate information based on frequency contributions, mitigating negative transfer. Extensive experiments on real-world datasets confirm FreMo's superior performance and generalization over state-of-the-art baselines, with its code publicly available.

Key takeaway

For Machine Learning Engineers developing urban transportation forecasting models, you should consider integrating frequency-domain approaches like FreMo. Its Modality-Wise Frequency Filter and Frequency-Guided Synergy Integrator offer a lightweight way to handle distinct spectral characteristics and uneven cross-modality interactions, potentially improving prediction accuracy and generalization over time-domain methods. Explore its plug-and-play compatibility with your existing time series backbones.

Key insights

Frequency-domain processing enables adaptive cross-modality synergy for superior multi-modality transportation forecasting.

Principles

• Disentangle spectral refinement from cross-modality synergy.
• Exploit frequency domain for adaptive interaction.
• Selectively aggregate information based on frequency contribution.

Method

FreMo uses a Modality-Wise Frequency Filter (MFF) to refine spectral components and a Frequency-Guided Synergy Integrator (FSI) to selectively aggregate cross-modality information based on frequency contributions, integrating with time series backbones.

In practice

• Integrate FreMo with existing time series backbones.
• Apply MFF to emphasize informative frequencies.
• Use FSI to mitigate negative transfer in multi-modal data.

Topics

• Multi-modality Transportation
• Frequency Domain Modeling
• Time Series Forecasting
• Cross-Modality Synergy
• Urban Systems
• Deep Learning

Code references

• beginner-sketch/FreMo
• Weijiang-Xiong/OpenSkyTraffic
• Master-PLC/FreDF
• Mashuaishuai669/SFDE

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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• DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems — DSFNet uses dual-domain spectral filtering to model complex spatio-temporal and cross-modality couplings in urban traffic.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.