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

2025-05-12

AI Summary

This study investigates the spatial evolution of the U.S. transportation cybersecurity ecosystem and the geographic distribution of skilled professionals, elucidating how socioeconomic factors shape industry clustering and dynamic visitor flows across automotive, logistics, transportation, and cybersecurity sectors. Method: We propose BiTransGCN—a novel hybrid framework that jointly integrates attention-based Transformers with Graph Convolutional Networks (GCNs) to model and forecast spatiotemporal visitor mobility and industrial clustering simultaneously. Contribution/Results: BiTransGCN achieves a 23.6% reduction in mean absolute error (MAE) over baseline models and identifies five high-potential regional industry clusters. The findings provide data-driven decision support for strategic investment in transportation critical infrastructure, cross-sector workforce planning, and evidence-based regional policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

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.

Problem

• Analyzing spatiotemporal visitor flow patterns in transportation cybersecurity
• Modeling industry clustering influenced by socioeconomic factors
• Predicting mobility trends for resilient transportation network planning

Innovation

• BiTransGCN framework combines Transformer and GCN
• AI forecasting integrates with spatial analysis
• Data-driven approach tracks industry clustering trends