The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908362942185472 |
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| author | Wang, Yuhao Wang, Kailai Hu, Songhua Yunpeng Zhang Lim, Gino Zhu, Pengyu |
| author_facet | Wang, Yuhao Wang, Kailai Hu, Songhua Yunpeng Zhang Lim, Gino Zhu, Pengyu |
| contents | The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics Wang, Yuhao Wang, Kailai Hu, Songhua Yunpeng Zhang Lim, Gino Zhu, Pengyu Computers and Society Machine Learning Physics and Society The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem. |
| title | The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics |
| topic | Computers and Society Machine Learning Physics and Society |
| url | https://arxiv.org/abs/2505.08822 |