mlsanityData to 14 Oct 2026

FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph

arXiv 2306.10945 ↗First posted 19 Jun 2023cs.LG cs.AIcited by 5

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Abstract (arXiv)

This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 minute), which is essential to traffic-related downstream applications. Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic. As a result, the traffic data is non-smooth between nodes, and hard to utilize previous methods which focus on smooth traffic data. To address this problem, we propose Fine-grained Deep Traffic Inference, termed as FDTI. Specifically, we construct a fine-grained traffic graph based on traffic signals to model the inter-road relations. Then, a physically-interpretable dynamic mobility convolution module is proposed to capture vehicle moving dynamics controlled by the traffic signals. Furthermore, traffic flow conservation is introduced to accurately infer future volume. Extensive experiments demonstrate that our method achieves state-of-the-art performance and learned traffic dynamics with good properties. To the best of our knowledge, we are the first to conduct the city-level fine-grained traffic prediction.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 19 Jun 2023.

Builds on

27 of 50 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  8. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 803
  9. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  10. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  11. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  12. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
15 more
  1. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  2. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
  4. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  5. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
  6. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 122
  7. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  8. 2019A Survey on Traffic Signal Control Methodscited by 79
  9. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  10. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  11. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  12. 2020FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecastingcited by 30
  13. 2021Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecastingcited by 19
  14. 2022Modeling Network-level Traffic Flow Transitions on Sparse Datacited by 12
  15. 2020Fine-Grained Urban Flow Inferencecited by 10

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Cited by year

Citing papers per year: 2023: 1; 2024: 4.

5 papers.

  1. 2023Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bankcited by 24
  2. 2024Dataset Condensation for Time Series Classification via Dual Domain Matchingcited by 9
  3. 2024CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecastingcited by 8
  4. 2024Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecastingcited by 7
  5. 2024Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecastingcited by 4