mlsanityData to 14 Oct 2026

Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation

arXiv 2507.19089 ↗First posted 25 Jul 2025cs.AI cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining lane-level traffic data has become a critical bottleneck for data-driven models due to limitations in the types and number of sensors and issues with the accuracy of tracking algorithms. To address this, we propose the Fine-grained Road Traffic Inference (FRTI) task, which aims to generate more detailed lane-level traffic information using limited road data, providing a more energy-efficient and cost-effective solution for precise traffic management. This task is abstracted as the first scene of the spatio-temporal graph node generation problem. We designed a two-stage framework--RoadDiff--to solve the FRTI task. solve the FRTI task. This framework leverages the Road-Lane Correlation Autoencoder-Decoder and the Lane Diffusion Module to fully utilize the limited spatio-temporal dependencies and distribution relationships of road data to accurately infer fine-grained lane traffic states. Based on existing research, we designed several baseline models with the potential to solve the FRTI task and conducted extensive experiments on six datasets representing different road conditions to validate the effectiveness of the RoadDiff model in addressing the FRTI task. The relevant datasets and code are available at https://github.com/ShuhaoLii/RoadDiff.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

22 of 46 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  7. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  8. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  9. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  10. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 185
  11. 2018Syntax-Directed Variational Autoencoder for Structured Datacited by 103
  12. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
10 more
  1. 2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 86
  2. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  3. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  4. 2019UrbanFM: Inferring Fine-Grained Urban Flowscited by 31
  5. 2019Graph Residual Flow for Molecular Graph Generationcited by 23
  6. 2023ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecastingcited by 22
  7. 2023Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learningcited by 20
  8. 2020Fine-Grained Urban Flow Inferencecited by 10
  9. 2024Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baselinecited by 3
  10. 2024MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Datacited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twinscited by 0
  2. 2026DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systemscited by 0
  3. 2026Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node Statescited by 0