arXiv AI

Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

arXiv:2604. 16084v2 Announce Type: replace-cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management.

arXiv AI
Jun 10

MoE Enhanced Federated Learning for Spatiotemporal Prediction

arXiv:2606. 10499v1 Announce Type: cross Abstract: Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor deployment and uneven urban development.

By Zhehao Dai, Xiao Han, Zhaolin Deng, Zijian Zhang, Xiangyu Zhao, Guojiang Shen, Xiangjie Kong
arXiv Machine Learning
Jun 9

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).

By Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu, Jiwon Kim, Constantinos Antoniou