Tensor decomposition for spatial-temporal traffic flow prediction with sparse data
Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meanin...
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sg-ntu-dr.10356-1456882021-01-05T02:17:48Z Tensor decomposition for spatial-temporal traffic flow prediction with sparse data Yang, Funing Liu, Guoliang Huang, Liping Chin, Cheng Siong School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Tensor Decomposition Traffic Flow Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance. Published version 2021-01-05T02:17:48Z 2021-01-05T02:17:48Z 2020 Journal Article Yang, F., Liu, G., Huang, L., & Chin, C. S. (2020). Tensor decomposition for spatial-temporal traffic flow prediction with sparse data. Sensors, 20(21), 6046-. doi:10.3390/s20216046 1424-8220 https://hdl.handle.net/10356/145688 10.3390/s20216046 33114275 21 20 en Sensors © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
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Engineering::Electrical and electronic engineering Tensor Decomposition Traffic Flow Yang, Funing Liu, Guoliang Huang, Liping Chin, Cheng Siong Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
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Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Yang, Funing Liu, Guoliang Huang, Liping Chin, Cheng Siong |
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Article |
author |
Yang, Funing Liu, Guoliang Huang, Liping Chin, Cheng Siong |
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Yang, Funing |
title |
Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
title_short |
Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
title_full |
Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
title_fullStr |
Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
title_full_unstemmed |
Tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
title_sort |
tensor decomposition for spatial-temporal traffic flow prediction with sparse data |
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2021 |
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https://hdl.handle.net/10356/145688 |
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1688665578040983552 |