A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction

TL;DR

This paper introduces GAMCN, a novel model combining graph convolutional networks and attention mechanisms to improve traffic speed prediction by capturing spatial and temporal traffic patterns more effectively.

Contribution

The paper presents a new graph and attentive multi-path convolutional network (GAMCN) that models spatial and temporal factors for traffic prediction, outperforming existing models.

Findings

Outperforms state-of-the-art models by up to 18.9% in prediction accuracy.

Achieves 23.4% improvement in prediction efficiency.

Effectively captures periodic traffic patterns using attention mechanisms.

Abstract

Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention} generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.