# Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network

arXiv: https://arxiv.org/abs/1811.00740 · HTML: https://mlsanity.com/paper/1811.00740/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools](https://mlsanity.com/paper/2103.11824/) (2021)
- [Edge-Centric Relational Reasoning for 3D Scene Graph Prediction](https://mlsanity.com/paper/2511.15288/) (2025)
- [Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction](https://mlsanity.com/paper/2305.19292/) (2023)
- [SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features](https://mlsanity.com/paper/2310.02282/) (2023)
- [Introduction to Graph Neural Networks for Machine Learning Engineers](https://mlsanity.com/paper/2412.19419/) (2024)

## Abstract (arXiv)

Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagation patterns in the graph. It could simultaneously predict traffic flow for all road segments based on the information gathered from the whole graph, which thus reduces the computational complexity significantly from O(nm) to O(n+m), while keeping the high accuracy. Moreover, it can also predict the variations of traffic trends. Experiments based on real-world data demonstrate that the proposed method outperforms the existing prediction methods.