The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics

TL;DR

This paper analyzes the spatial and temporal dynamics of transportation cybersecurity sectors in the US, using AI models to predict visitor flows and industry clustering for strategic planning and resilience.

Contribution

It introduces the BiTransGCN framework combining Transformer and Graph Convolutional Networks to model and forecast industry spatial dynamics.

Findings

Identification of distinct industry clusters and visitor flow patterns.

Enhanced prediction accuracy of spatial industry movements.

Insights into socioeconomic factors influencing sector clustering.

Abstract

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.