用AI分析交通网络的网络安全人流与集群分布，助力安全基建布局
The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
- 构建BiTransGCN模型融合Transformer与图卷积，预测人流与产业聚集
- 揭示美国交通网络安全领域存在显著空间集群与人群流动模式
- 适合政策制定者与交通网络安全企业做战略规划参考
交通网络安全生态系统正快速发展，涵盖网络安全、汽车及交通运输物流等领域，将在美国形成独特的空间集群和访客流动模式。本研究分析了访客流动的时空动态，探讨社会经济因素如何影响这些新兴领域的产业聚集与人才分布。为建模和预测访客流动模式，我们提出一种基于注意力机制的Transformer与图卷积网络结合的BiTransGCN框架。通过将AI预测技术与空间分析融合，本研究提升了对产业聚集与移动趋势变化的追踪、解读与预判能力，为构建安全韧性交通网络提供支持，同时为经济规划、人才培养与针对性投资提供数据驱动基础。
原文摘要 · Abstract (English)
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.
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