A comprehensive study of speed prediction in transportation system: From vehicle to traffic

• Published in: iScience Vol. 25; no. 3; p. 103909
• Main Authors: Zhou, Zewei, Yang, Ziru, Zhang, Yuanjian, Huang, Yanjun, Chen, Hong, Yu, Zhuoping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 18.03.2022; Elsevier
• Subjects: Algorithms; Engineering; Review; Transportation engineering; Engineering; Transportation engineering; Algorithms
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2022.103909

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. [Display omitted] •A comprehensive review is provided for speed prediction in and between different levels•Existing speed prediction methods at different levels are systematically surveyed•The future directions of speed prediction in the transportation system are elaborated Algorithms; Engineering; Transportation engineering