# Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

arXiv: https://arxiv.org/abs/1705.02699 · HTML: https://mlsanity.com/paper/1705.02699/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2017.

## Built on by (most cited first, 30 of 35)

- [Learning dynamic and hierarchical traffic spatiotemporal features with Transformer](https://mlsanity.com/paper/2104.05163/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting](https://mlsanity.com/paper/2208.03063/) (2022)
- [Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2104.13096/) (2021)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]](https://mlsanity.com/paper/2201.03244/) (2022)
- [Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference](https://mlsanity.com/paper/2102.03980/) (2021)
- [Spatiotemporal Data Mining: A Survey](https://mlsanity.com/paper/2206.12753/) (2022)
- [Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data](https://mlsanity.com/paper/2309.12574/) (2023)
- [Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting](https://mlsanity.com/paper/2206.11078/) (2022)
- [TrafFormer: A Transformer Model for Predicting Long-term Traffic](https://mlsanity.com/paper/2302.12388/) (2023)
- [DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting](https://mlsanity.com/paper/2307.00518/) (2023)
- [Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models](https://mlsanity.com/paper/2502.18652/) (2025)
- [Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction](https://mlsanity.com/paper/2209.03858/) (2022)
- [Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking](https://mlsanity.com/paper/2301.04233/) (2023)
- [SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation](https://mlsanity.com/paper/2306.07292/) (2023)
- [Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations](https://mlsanity.com/paper/2405.14257/) (2024)
- [Traffic Prediction considering Multiple Levels of Spatial-temporal Information: A Multi-scale Graph Wavelet-based Approach](https://mlsanity.com/paper/2406.13038/) (2024)
- [A Multi-Layer CNN-GRUSKIP model based on transformer for spatial TEMPORAL traffic flow prediction](https://mlsanity.com/paper/2501.07593/) (2025)
- [Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction](https://mlsanity.com/paper/2602.21757/) (2026)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)](https://mlsanity.com/paper/2101.09356/) (2021)
- [Bayesian Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2104.00488/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)](https://mlsanity.com/paper/2104.14406/) (2021)
- [Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)](https://mlsanity.com/paper/2108.03400/) (2021)
- [Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks](https://mlsanity.com/paper/2204.10222/) (2022)
- [A Transferable Intersection Reconstruction Network for Traffic Speed Prediction](https://mlsanity.com/paper/2207.11030/) (2022)
- [Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach](https://mlsanity.com/paper/2211.03033/) (2022)
- [Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach](https://mlsanity.com/paper/2310.12353/) (2023)
- [Short-term prediction of construction waste transport activities using AI-Truck](https://mlsanity.com/paper/2312.04609/) (2023)
- [Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach](https://mlsanity.com/paper/2312.05576/) (2023)

## 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.