基于图卷积网络的交通流预测方法综述

近年来,基于深度学习的交通流预测方法一直是交通流预测领域的研究热点.与传统卷积神经网络不同,适合处理非欧几里得数据的图卷积网络在空间特征建模方面表现出了强大的能力,而反映路网空间特征的拓扑图、距离图、流量相似图等正是典型的非欧几里得数据.因此,基于图卷积网络及其变体的交通流预测方法成为交通流预测领域的一个研究热点,并取得了很多有吸引力的研究结果.本文对近年来基于图卷积网络的交通流预测模型进行了分类和总结.首先,从图卷积网络的基本定义出发,结合空域图卷积和谱域图卷积的定义详述了图卷积的基本原理.其次,根据预测模型的网络结构特点,将基于图卷积网络的交通流预测模型分为“组合型”和“改进型”两大类,并对其中最具代表性的模型结构进行了详细分析和讨论. 此外,对交通流预测领域中常用于模型性能对比的典型数据集进行了综述,并以其中一个真实数据集为例开展仿真测试,展示了4个基于图卷积网络交通流预测模型的预测性能.最后,基于当前的研究现状和发展趋势,对基于图卷积网络的交通流预测方法研究领域中未来的研究热点和难点进行了开放性的讨论和展望.

A survey of traffic flow prediction based on graph convolutional networks

In recent years,deep learning has been a hot research topic in traffic flow prediction.Graph convolutional networks outperform traditional convolutional neural networks in spatial feature modeling,in view of their powerful capabilities in processing non-Euclidean data such as topological map,distance map and flow similarity map.Therefore,graph convolutional network and its variants have become a research hotspot in traffic flow prediction,and many attractive research results have been obtained.This article classifies and summarizes traffic flow prediction models based on graph convolutional networks in recent years.First,the graph convolution is elaborated by combining the definitions of spatial convolution and spectral convolution.Second,in view of the network structure of the prediction model,the graph convolutional network based traffic flow prediction models are divided into two major categories of combined type and improved type,each of which are analyzed and discussed in detail with representative model structures.In addition,typical datasets commonly used in traffic flow prediction for model performance comparison are reviewed,and a simulation test is conducted using one real dataset to demonstrate the prediction performance of four traffic flow prediction models based on graph convolutional networks.Finally,the future research hotspots and challenges in traffic flow prediction based on graph convolutional networks are prospected.