mlsanityData to 13 Oct 2026

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

arXiv 1705.02699 ↗First posted 7 May 2017cs.LGcited by 35

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Abstract (arXiv)

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Key facts. Cited by 35 papers in the map (data through 13 Oct 2026). First posted on arXiv 7 May 2017.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 8; 2022: 8; 2023: 9; 2024: 5; 2025: 3; 2026: 2.

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  1. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  2. 2023Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunitiescited by 16
  3. 2022Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecastingcited by 11
  4. 2021Graph Neural Networks for Traffic Forecastingcited by 7
  5. 2021Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flowcited by 4
  6. 2022GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]cited by 4
  7. 2021Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inferencecited by 3
  8. 2022Spatiotemporal Data Mining: A Surveycited by 3
  9. 2023Classification of Alzheimers Disease with Deep Learning on Eye-tracking Datacited by 3
  10. 2022Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecastingcited by 2
  11. 2023TrafFormer: A Transformer Model for Predicting Long-term Trafficcited by 2
  12. 2023DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecastingcited by 2
23 more
  1. 2025Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Modelscited by 2
  2. 2022Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Predictioncited by 1
  3. 2023Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Maskingcited by 1
  4. 2023SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregationcited by 1
  5. 2024Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurationscited by 1
  6. 2024Traffic Prediction considering Multiple Levels of Spatial-temporal Information: A Multi-scale Graph Wavelet-based Approachcited by 1
  7. 2025A Multi-Layer CNN-GRUSKIP model based on transformer for spatial TEMPORAL traffic flow predictioncited by 1
  8. 2026Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Predictioncited by 1
  9. 2021Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)cited by 0
  10. 2021Bayesian Graph Convolutional Network for Traffic Predictioncited by 0
  11. 2021Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)cited by 0
  12. 2021Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)cited by 0
  13. 2022Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networkscited by 0
  14. 2022A Transferable Intersection Reconstruction Network for Traffic Speed Predictioncited by 0
  15. 2022Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approachcited by 0
  16. 2023Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approachcited by 0
  17. 2023Short-term prediction of construction waste transport activities using AI-Truckcited by 0
  18. 2023Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approachcited by 0
  19. 2024MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory datacited by 0
  20. 2024LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edgecited by 0
  21. 2024Incorporating Long-term Data in Training Short-term Traffic Prediction Modelcited by 0
  22. 2025Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecastingcited by 0
  23. 2026CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Predictioncited by 0