Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network

TL;DR

This paper introduces a graph recurrent neural network with a novel linkage network framework for efficient and accurate metropolitan traffic prediction, reducing computational complexity and capturing traffic trend variations.

Contribution

It proposes the Linkage Network model and a Graph Recurrent Neural Network that significantly reduce computational complexity while maintaining high prediction accuracy.

Findings

Outperforms existing traffic prediction methods on real-world data.

Reduces computational complexity from O(nm) to O(n+m).

Effectively predicts traffic trend variations.

Abstract

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.