# Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting

arXiv: https://arxiv.org/abs/1909.11197 · HTML: https://mlsanity.com/paper/1909.11197/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)](https://mlsanity.com/paper/2305.05237/) (2023)
- [Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2204.01618/) (2022)
- [Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems](https://mlsanity.com/paper/2210.06404/) (2022)
- [Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning](https://mlsanity.com/paper/2310.10374/) (2023)
- [Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2401.06040/) (2024)
- [PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training](https://mlsanity.com/paper/2507.11683/) (2025)
- [PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting](https://mlsanity.com/paper/2412.13607/) (2024)
- [S2Transformer: Scalable Structured Transformers for Global Station Weather Forecasting](https://mlsanity.com/paper/2509.19648/) (2025)
- [Long Short-term Cognitive Networks](https://mlsanity.com/paper/2106.16233/) (2021)
- [Structured Time Series Prediction without Structural Prior](https://mlsanity.com/paper/2202.03539/) (2022)
- [A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios](https://mlsanity.com/paper/2308.15464/) (2023)
- [Towards Scalable and Structured Spatiotemporal Forecasting](https://mlsanity.com/paper/2509.18115/) (2025)
- [Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction](https://mlsanity.com/paper/2602.22274/) (2026)
- [Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction](https://mlsanity.com/paper/2609.31667/) (2026)

## Abstract (arXiv)

Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic. Despite the promising results, however, applying DCRNNs for large highway networks still remains elusive because of computational and memory bottlenecks. We present an approach for implementing a DCRNN for a large highway network that overcomes these limitations. Our approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently. We demonstrate the efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11,160 sensor locations. We develop an overlapping nodes approach for the graph-partitioning-based DCRNN to include sensor locations from partitions that are geographically close to a given partition. Furthermore, we demonstrate that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics. Our approach to developing DCRNN models that represent large highway networks can be a potential core capability in advanced highway traffic monitoring systems, where a trained DCRNN model forecasting traffic at all sensor locations can be used to adjust traffic management strategies proactively based on anticipated future conditions.