Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks
Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility...
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| Vydáno v: | IEEE transactions on intelligent transportation systems Ročník 22; číslo 12; s. 7474 - 7484 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1524-9050, 1558-0016 |
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| Abstract | Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility patterns. However, existing models or approaches neglect the network structure of spatial flows, thus resulting in inappropriate estimates and a low performance. The development of graph neural networks offers a powerful tool to deal with graph-structured data. In this article, we proposed a spatial interaction graph convolutional network model, which combines graph convolution and a mapping function to predict flow data from the perspective of network learning. This model utilizes geographical unit embedding in local spatial networks to improve prediction accuracy. A negative sampling technique is adopted to reduce misestimation. Experiments on Beijing taxi trip data verified the usefulness of our model in spatial flow prediction. We also demonstrated that a biased training sample had a negative impact on the model's performance. More attributes of geographical units, a more proper negative sampling rate and a larger training set can increase the prediction accuracy of flow data. |
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| AbstractList | Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility patterns. However, existing models or approaches neglect the network structure of spatial flows, thus resulting in inappropriate estimates and a low performance. The development of graph neural networks offers a powerful tool to deal with graph-structured data. In this article, we proposed a spatial interaction graph convolutional network model, which combines graph convolution and a mapping function to predict flow data from the perspective of network learning. This model utilizes geographical unit embedding in local spatial networks to improve prediction accuracy. A negative sampling technique is adopted to reduce misestimation. Experiments on Beijing taxi trip data verified the usefulness of our model in spatial flow prediction. We also demonstrated that a biased training sample had a negative impact on the model's performance. More attributes of geographical units, a more proper negative sampling rate and a larger training set can increase the prediction accuracy of flow data. |
| Author | Yao, Xin Gao, Yong Liu, Yu Zhu, Di Manley, Ed Wang, Jiaoe |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0000-0002-0109-2643 surname: Yao fullname: Yao, Xin email: yaoxin@pku.edu.cn organization: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China – sequence: 2 givenname: Yong orcidid: 0000-0003-1562-6228 surname: Gao fullname: Gao, Yong email: gaoyong@pku.edu.cn organization: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China – sequence: 3 givenname: Di orcidid: 0000-0002-3237-6032 surname: Zhu fullname: Zhu, Di email: patrick.zhu@pku.edu.cn organization: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China – sequence: 4 givenname: Ed orcidid: 0000-0002-8904-0513 surname: Manley fullname: Manley, Ed email: e.j.manley@leeds.ac.uk organization: School of Geography, University of Leeds, Leeds, U.K – sequence: 5 givenname: Jiaoe surname: Wang fullname: Wang, Jiaoe email: wangje@igsnrr.ac.cn organization: China Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, Beijing, China – sequence: 6 givenname: Yu orcidid: 0000-0002-0016-2902 surname: Liu fullname: Liu, Yu email: liuyu@urban.pku.edu.cn organization: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China |
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| SubjectTerms | Artificial neural networks Biological system modeling Convolution Data collection data imputation Data models graph convolution graph embedding Graph neural networks Gravity Neural networks Origin-destination flow Predictive models Sampling methods Spatial databases spatial interaction network Structured data Training |
| Title | Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks |
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