Optimal Traffic Sensor Location for Origin-Destination Estimation Using a Compressed Sensing Framework | |
Ye, Peijun1,2![]() | |
Source Publication | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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ISSN | 1524-9050 |
2017-07-01 | |
Volume | 18Issue:7Pages:1857-1866 |
Subtype | Article |
Abstract | A series of flow estimation problems, especially origin-destination estimation, involves optimally locating sensors on a transportation network to measure traffic counts. As compressed sensing (CS) provides a new method to solve the estimation problem, its sensor location strategy needs to be researched in order to facilitate the reconstruction. This paper first points out that the accurate flow recovery is difficult by introducing a necessary condition, and then categorizes the location determination into two cases: sensor number with restriction and without restriction. For both cases, we elucidate their theoretical foundations of locating methods and propose an algorithm based on column coherence minimization, which optimally facilitates the reconstruction for CS framework. Numerical experiments indicate that the selected sensor locations fit the flow recovery and the proposed algorithm, compared with other methods, can lead to a slightly better result under certain observations. |
Keyword | Traffic Sensor Location Traffic Flow Estimation Compressed Sensing |
WOS Headings | Science & Technology ; Technology |
DOI | 10.1109/TITS.2016.2614828 |
WOS Keyword | LINK FLOW INFERENCE ; MATRIX ESTIMATION ; SELECTION ; NETWORKS ; MODEL ; OBSERVABILITY ; OPTIMIZATION ; QUALITY ; COUNTS |
Indexed By | SCI |
Language | 英语 |
Funding Organization | National Natural Science Foundation of China(61603381) |
WOS Research Area | Engineering ; Transportation |
WOS Subject | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS ID | WOS:000404370500017 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/15225 |
Collection | 复杂系统管理与控制国家重点实验室_智能化团队 |
Corresponding Author | Ye, Peijun |
Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China 3.Natl Univ Def & Technol, Mil Computat Expt & Parallel Syst Res Ctr, Changsha 410073, Hunan, Peoples R China |
First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Recommended Citation GB/T 7714 | Ye, Peijun,Wen, Ding. Optimal Traffic Sensor Location for Origin-Destination Estimation Using a Compressed Sensing Framework[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2017,18(7):1857-1866. |
APA | Ye, Peijun,&Wen, Ding.(2017).Optimal Traffic Sensor Location for Origin-Destination Estimation Using a Compressed Sensing Framework.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,18(7),1857-1866. |
MLA | Ye, Peijun,et al."Optimal Traffic Sensor Location for Origin-Destination Estimation Using a Compressed Sensing Framework".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 18.7(2017):1857-1866. |
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07726026.pdf(1590KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download |
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