Citation

Zhou Z, Yang Z, Zhang Y, Huang Y, Chen H, Yu Z. iScience 2022; 25(3): e103909.

Copyright

(Copyright © 2022, Cell Press)

DOI

10.1016/j.isci.2022.103909

PMID

35281740

PMCID

PMC8904620

Abstract

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.

Language: en

Keywords

Algorithms; Transportation engineering; Engineering