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Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data
Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, ma...
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| Published in: | PloS one 2016-07, Vol.11 (7), p.e0157420-e0157420 |
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| creator | Ran, Bin Song, Li Zhang, Jian Cheng, Yang Tan, Huachun |
| description | Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. |
| doi_str_mv | 10.1371/journal.pone.0157420 |
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The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0157420</identifier><identifier>PMID: 27448326</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Artificial neural networks ; Automobiles ; Aviation ; Biology and Life Sciences ; Collaboration ; Computer and Information Sciences ; Computer Simulation ; Correlation ; Engineering ; Engineering and Technology ; Floating structures ; Information technology ; Intelligent transportation systems ; Internet of Things ; Laboratories ; Mathematical problems ; Measurement ; Methods ; Missing data ; Penetration ; People and places ; Physical Sciences ; Research and Analysis Methods ; Roads ; Sensors ; Social Sciences ; Spatial distribution ; State estimation ; Traffic ; Traffic estimation ; Traffic information ; Traffic models ; Transportation ; Transportation networks ; Vehicles</subject><ispartof>PloS one, 2016-07, Vol.11 (7), p.e0157420-e0157420</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Ran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Automobiles</subject><subject>Aviation</subject><subject>Biology and Life Sciences</subject><subject>Collaboration</subject><subject>Computer and Information Sciences</subject><subject>Computer Simulation</subject><subject>Correlation</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Floating structures</subject><subject>Information technology</subject><subject>Intelligent transportation systems</subject><subject>Internet of Things</subject><subject>Laboratories</subject><subject>Mathematical problems</subject><subject>Measurement</subject><subject>Methods</subject><subject>Missing 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Huachun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-07-22</date><risdate>2016</risdate><volume>11</volume><issue>7</issue><spage>e0157420</spage><epage>e0157420</epage><pages>e0157420-e0157420</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><notes>Conceived and designed the experiments: BR. Performed the experiments: LS. Analyzed the data: JZ. Contributed reagents/materials/analysis tools: YC. Wrote the paper: LS. Designed the framework of this paper: BR. Revised the manuscript: HT.</notes><notes>Competing Interests: The authors have declared that no competing interests exist.</notes><abstract>Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27448326</pmid><doi>10.1371/journal.pone.0157420</doi><tpages>e0157420</tpages><orcidid>https://orcid.org/0000-0002-9086-7622</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Artificial neural networks Automobiles Aviation Biology and Life Sciences Collaboration Computer and Information Sciences Computer Simulation Correlation Engineering Engineering and Technology Floating structures Information technology Intelligent transportation systems Internet of Things Laboratories Mathematical problems Measurement Methods Missing data Penetration People and places Physical Sciences Research and Analysis Methods Roads Sensors Social Sciences Spatial distribution State estimation Traffic Traffic estimation Traffic information Traffic models Transportation Transportation networks Vehicles |
| title | Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data |
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