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

Zewei Zhou; Ziru Yang; Yuanjian Zhang; Yanjun Huang; Hong Chen; Zhuoping Yu

doi:10.1016/j.isci.2022.103909

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

iScience. 2022 Feb 12;25(3):103909. doi: 10.1016/j.isci.2022.103909. eCollection 2022 Mar 18.

Authors

Zewei Zhou¹, Ziru Yang¹, Yuanjian Zhang², Yanjun Huang¹, Hong Chen³, Zhuoping Yu¹

Affiliations

¹ School of Automotive Studies, Tongji University, Shanghai 201804, China.
² Department of Aeronautical and Automotive Engineering, Loughborough University, LoughboroughLE11 3TU, UK.
³ College of Electronics and Information Engineering, Tongji University, Shanghai201804, China.

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.

Keywords: Algorithms; Engineering; Transportation engineering.

Publication types

Review