A comprehensive study of speed prediction in transportation system: From vehicle to traffic
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 pr...
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| Published in | iScience Vol. 25; no. 3; p. 103909 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
18.03.2022
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2589-0042 2589-0042 |
| DOI | 10.1016/j.isci.2022.103909 |
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| 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.
[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 |
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| AbstractList | 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. 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 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. • 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 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.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. |
| ArticleNumber | 103909 |
| Author | Huang, Yanjun Zhou, Zewei Yang, Ziru Zhang, Yuanjian Yu, Zhuoping Chen, Hong |
| Author_xml | – sequence: 1 givenname: Zewei orcidid: 0000-0002-7378-9810 surname: Zhou fullname: Zhou, Zewei organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 2 givenname: Ziru orcidid: 0000-0003-3497-6119 surname: Yang fullname: Yang, Ziru organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 3 givenname: Yuanjian orcidid: 0000-0001-5563-8480 surname: Zhang fullname: Zhang, Yuanjian organization: Department of Aeronautical and Automotive Engineering, Loughborough University, LoughboroughLE11 3TU, UK – sequence: 4 givenname: Yanjun orcidid: 0000-0003-3133-8031 surname: Huang fullname: Huang, Yanjun email: yanjun_huang@tongji.edu.cn organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 5 givenname: Hong orcidid: 0000-0002-1724-8649 surname: Chen fullname: Chen, Hong organization: College of Electronics and Information Engineering, Tongji University, Shanghai201804, China – sequence: 6 givenname: Zhuoping surname: Yu fullname: Yu, Zhuoping organization: School of Automotive Studies, Tongji University, Shanghai 201804, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35281740$$D View this record in MEDLINE/PubMed |
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| Title | A comprehensive study of speed prediction in transportation system: From vehicle to traffic |
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