arXiv AI

STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

STHMoE is a Spatio‑Temporal Hypergraph‑Enhanced Mixture of Experts framework designed for urban traffic forecasting. It separates traffic dynamics into frequency‑, time‑, spatial‑, and higher‑order representations, each handled by a prompt‑guided expert built on a partially frozen large language model. The higher‑order expert uses an adaptive hypergraph module to learn evolving spatial structures, while an entropy‑aware router balances expert usage and fuses outputs, achieving competitive results on ten real‑world traffic benchmarks.

arXiv AI

Sep 18

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

FedeRICo is a federated traffic forecasting framework that addresses heterogeneity across client sensor subgraphs by combining gradient-level collaboration with boundary-aware residual communication. It uses a dual-branch architecture: a globally guided branch for transferable forecasting structure and a private residual branch that preserves client-specific corrections and incorporates boundary residual signals. Experiments on four real-world traffic benchmarks show that FedeRICo outperforms state‑of‑the‑art federated spatial‑temporal baselines while keeping training runtime competitive.

By Fermin Orozco, Man Luo, Johan Wahlstr\"om

benchmarks safety

arXiv Machine Learning

Sep 15

A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting

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

diffusion benchmarks

arXiv AI

Sep 24

Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting

The paper introduces LoReST, a Local-Region Spatial Temporal network designed for large-scale traffic forecasting. LoReST captures local spatial heterogeneity by relation-aware aggregation within node neighborhoods and incorporates cross-region context through mean pooling, inter-region attention, and broadcasting back to nodes. Experiments on the LargeST benchmark demonstrate significant improvements, reducing MAE, RMSE, and MAPE by 4.78%, 3.60%, and 5.75% respectively.

By Qi Feng, Zidong Wang, Bo Li, Xiaoguang Gao, Jiayu Zhang, Chenfeng Wang, Kaifang Wan

benchmarks

arXiv AI

Jun 9

DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems

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

benchmarks

arXiv AI

Oct 1

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

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

agents benchmarks

arXiv AI

Jul 9

STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting

arXiv:2607. 06614v1 Announce Type: cross Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks.

By Ye Zihao

llms agents benchmarks

arXiv AI

Aug 6

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

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

llms

arXiv AI

Aug 3

SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.

By Yongfeng Su, Hongwen Li, Zijian Zhang, Ziquan Fang, Lu Chen, Christian S. Jensen, Hong Gao, Yinjun Han

llms

Hugging Face Trending Papers

Sep 23

Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting

Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial dependencies and cross-region context.Spatial depen...

benchmarks

arXiv AI

4d ago

GAS-PFN: Graph-Aware Spatiotemporal Prior-Data Fitted Networks for Zero-Shot Traffic Forecasting

The paper introduces GAS-PFN, a Graph‑Aware Spatiotemporal Prior‑Data Fitted Network that incorporates road topology into synthetic task priors via random‑walk diffusion and uses a two‑mask Transformer architecture to separately model sensor‑specific temporal dynamics and spatiotemporal interactions. This design allows the model to adapt to new traffic networks without parameter updates, achieving a 34.0% average MSE reduction over TimePFN and a 27.7% reduction over recent industry zero‑shot forecasters across six benchmarks at a 90‑minute horizon.

By Joseph Natter, Long Vu, Yifan Zhang, Bobin Deng, Kun Suo, Rui Wu

llms diffusion benchmarks

arXiv AI

Jul 16

STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting

arXiv:2607. 13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting.

By Sicong Lai, Yuehong Hu, Siru Zhong, Si Qiao, Yuxuan Liang, Guangyin Jin

benchmarks

arXiv AI

Aug 17

Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction

arXiv:2608. 14177v1 Announce Type: cross Abstract: Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies.

By Xuanmian He, Can Li, Wanjing Ma

benchmarks safety

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