计算机科学
人工神经网络
图形
智能交通系统
人工智能
数据科学
交通生成模型
机器学习
运输工程
工程类
理论计算机科学
计算机网络
作者
Weiwei Jiang,Jiayun Luo,M. He,Weixi Gu
摘要
Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.
科研通智能强力驱动
Strongly Powered by AbleSci AI