Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model
The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical dis...
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/4/172 |
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| author | Zehao Yuan Xuanyan Chen Biyu Chen Yubo Luo Yu Zhang Wenxin Teng Chao Zhang |
| author_facet | Zehao Yuan Xuanyan Chen Biyu Chen Yubo Luo Yu Zhang Wenxin Teng Chao Zhang |
| author_sort | Zehao Yuan |
| collection | DOAJ |
| description | The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin–Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications. |
| format | Article |
| id | doaj-art-6dc08fcf9f4b44d3be909f5a35a6b40e |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-6dc08fcf9f4b44d3be909f5a35a6b40e2025-08-20T02:28:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114417210.3390/ijgi14040172Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks ModelZehao Yuan0Xuanyan Chen1Biyu Chen2Yubo Luo3Yu Zhang4Wenxin Teng5Chao Zhang6State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaHangzhou Institute of Technology, Xidian University, Hangzhou 311200, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin–Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications.https://www.mdpi.com/2220-9964/14/4/172origin–destination matrixgenerative adversarial networksintelligent transportation systemsgravity modelradiation |
| spellingShingle | Zehao Yuan Xuanyan Chen Biyu Chen Yubo Luo Yu Zhang Wenxin Teng Chao Zhang Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model ISPRS International Journal of Geo-Information origin–destination matrix generative adversarial networks intelligent transportation systems gravity model radiation |
| title | Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model |
| title_full | Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model |
| title_fullStr | Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model |
| title_full_unstemmed | Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model |
| title_short | Generating Large-Scale Origin–Destination Matrix via Progressive Growing Generative Adversarial Networks Model |
| title_sort | generating large scale origin destination matrix via progressive growing generative adversarial networks model |
| topic | origin–destination matrix generative adversarial networks intelligent transportation systems gravity model radiation |
| url | https://www.mdpi.com/2220-9964/14/4/172 |
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