Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme
Real-time and accurate network-wide traffic volume estimation/detection is an essential part of urban transport system planning and management. As it is impractical to install detectors on every road segment of the city network, methods on the network-wide flow estimation based on limited detector d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023011https://www.aimspress.com/article/doi/10.3934/era.2023011 |
| _version_ | 1811169629832216576 |
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| author | Jiping Xing Yunchi Wu Di Huang Xin Liu |
| author_facet | Jiping Xing Yunchi Wu Di Huang Xin Liu |
| author_sort | Jiping Xing |
| collection | DOAJ |
| description | Real-time and accurate network-wide traffic volume estimation/detection is an essential part of urban transport system planning and management. As it is impractical to install detectors on every road segment of the city network, methods on the network-wide flow estimation based on limited detector data are of considerable significance. However, when the plan of detector deployment is uncertain, existing methods are unsuitable to be directly used. In this study, a transfer component analysis (TCA)-based network-wide volume estimation model, considering the different traffic volume distributions of road segments and transforming traffic features into common data space, is proposed. Moreover, this study applied taxi GPS (global positioning system) data and cellular signaling data with the same spatio-temporal coverage to improve feature extraction. In numerical experiments, the robustness and stability of the proposed network-wide estimation method outperformed other baselines in the two subnetworks selected from the urban centers and suburbs. |
| first_indexed | 2024-04-10T16:45:27Z |
| format | Article |
| id | doaj.art-7364e1579b39490886178e0414ad4fd1 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-04-10T16:45:27Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-7364e1579b39490886178e0414ad4fd12023-02-08T00:48:59ZengAIMS PressElectronic Research Archive2688-15942023-01-0131120722810.3934/era.2023011Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment schemeJiping Xing0Yunchi Wu1Di Huang2Xin Liu31. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China2. School of Public Administration, Huazhong University of Science and Technology, Wuhan, China1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, China1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, ChinaReal-time and accurate network-wide traffic volume estimation/detection is an essential part of urban transport system planning and management. As it is impractical to install detectors on every road segment of the city network, methods on the network-wide flow estimation based on limited detector data are of considerable significance. However, when the plan of detector deployment is uncertain, existing methods are unsuitable to be directly used. In this study, a transfer component analysis (TCA)-based network-wide volume estimation model, considering the different traffic volume distributions of road segments and transforming traffic features into common data space, is proposed. Moreover, this study applied taxi GPS (global positioning system) data and cellular signaling data with the same spatio-temporal coverage to improve feature extraction. In numerical experiments, the robustness and stability of the proposed network-wide estimation method outperformed other baselines in the two subnetworks selected from the urban centers and suburbs.https://www.aimspress.com/article/doi/10.3934/era.2023011https://www.aimspress.com/article/doi/10.3934/era.2023011network-wide volume estimationtransfer component analysismulti-source data fusiontaxi gps datacellular signaling data |
| spellingShingle | Jiping Xing Yunchi Wu Di Huang Xin Liu Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme Electronic Research Archive network-wide volume estimation transfer component analysis multi-source data fusion taxi gps data cellular signaling data |
| title | Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme |
| title_full | Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme |
| title_fullStr | Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme |
| title_full_unstemmed | Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme |
| title_short | Transfer learning for robust urban network-wide traffic volume estimation with uncertain detector deployment scheme |
| title_sort | transfer learning for robust urban network wide traffic volume estimation with uncertain detector deployment scheme |
| topic | network-wide volume estimation transfer component analysis multi-source data fusion taxi gps data cellular signaling data |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023011https://www.aimspress.com/article/doi/10.3934/era.2023011 |
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