Zhou Zewei, Yang Ziru, Zhang Yuanjian, Huang Yanjun, Chen Hong, Yu Zhuoping
School of Automotive Studies, Tongji University, Shanghai 201804, China.
Department of Aeronautical and Automotive Engineering, Loughborough University, LoughboroughLE11 3TU, UK.
iScience. 2022 Feb 12;25(3):103909. doi: 10.1016/j.isci.2022.103909. eCollection 2022 Mar 18.
In the intelligent transportation system (ITS), speed prediction plays a significant role in supporting vehicle routing and traffic guidance. Recently, a considerable amount of research has been devoted to a single-level (e.g., traffic or vehicle) prediction. However, a systematic review of speed prediction in and between different levels is still missing. In this article, existing research is comprehensively analyzed and divided into three levels, i.e. macro traffic, micro vehicles, and meso lane. In addition, this article summarizes the influencing factors and reviews the prediction methods based on how those methods utilize the available information to meet the challenges of the prediction at different levels. This is followed by a summary of evaluation metrics, public datasets, and open-source codes. Finally, future directions in this field are discussed to inspire and guide readers. This article aims to draw a complete picture of speed prediction and promote the development of ITS.
在智能交通系统（ITS）中，速度预测在支持车辆路径规划和交通引导方面发挥着重要作用。近年来，大量研究致力于单一层级（如交通或车辆）的预测。然而，对不同层级内部和之间的速度预测仍缺乏系统的综述。在本文中，对现有研究进行了全面分析，并分为三个层级，即宏观交通、微观车辆和中观车道。此外，本文总结了影响因素，并根据这些方法如何利用可用信息来应对不同层级预测的挑战对预测方法进行了综述。接下来是评估指标、公共数据集和开源代码的总结。最后，讨论了该领域的未来发展方向，以启发和引导读者。本文旨在全面呈现速度预测的情况，并推动智能交通系统的发展。
