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:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
By Yongchao Li, Yang Li, Zhuoxuan Li, Jun Chen, Chu Zhang, Jinde Cao, Leszek Rutkowski
arXiv:2602.14049v2 Announce Type: replace-cross
Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...
By Yue Wang, Areg Karapetyan, Djellel Difallah, Samer Madanat
The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets demonstrate that DF-LLM outperforms existing methods in predictive accuracy.
By Xue Qiu, Jianli Xiao
arXiv:2609.13878v1 Announce Type: new
Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...
By Zhouyang Liu, Jindong Han, Hao Wang, Xinyue Liu, Hui Gao, Dongsheng Li, Hao Liu
arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.
By Laha Ale, Letian Lin, Na Cao, Zheng Ma, Peng Yu