arXiv AI

Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting

arXiv:2607. 07016v1 Announce Type: cross Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems.

Hugging Face Trending Papers
Jul 8

Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting

Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Real-world traffic often exhibits bursty endogenous dynamics and disturbances triggered by external urban events, which makes reliable prediction highly challenging.

arXiv Machine Learning
Jul 10

Frequency-Domain Multi-Modality Transportation Modeling

arXiv:2607. 08475v1 Announce Type: new Abstract: 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.

By Jiewen Deng, Hangchen Liu, Junchen Li, Boyuan Zhang, Renhe Jiang
Hugging Face Trending Papers
Jul 9

Frequency-Domain Multi-Modality Transportation Modeling

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.