The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
It addresses strategic planning for a secure and resilient transportation network, offering data-driven insights for economic planning and workforce development, but is incremental as it applies existing AI methods to a new domain-specific problem.
This study tackled the problem of understanding and predicting spatial clusters and visitor flow patterns in the US transportation cybersecurity ecosystem by developing a BiTransGCN framework, which integrates Transformer and Graph Convolutional Network models to improve tracking and anticipation of industry and mobility trends.
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.