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

Yuhao Wang, Kailai Wang, Songhua Hu, Yunpeng, Zhang, Gino Lim, Pengyu Zhu

TL;DR

This study addresses how three transportation cybersecurity–related industries (cybersecurity, automotive, and transportation and logistics) spatially cluster and how visitor flows evolve across the United States. It introduces BiTransGCN, a hybrid Transformer–GCN model, to forecast long-term, non-Euclidean spatiotemporal visitor flows by integrating SafeGraph origin–destination data with American Community Survey socioeconomics and NAICS industry categories. Through Global Moran’s I, GeoShapley, and SHAP-based analyses, the work links geolocation, education, and other social factors to dynamic clustering patterns and predicts a US-wide average visitor-flow increase of $14.16\%$, with industry-specific trajectories. The findings offer actionable insights for economic planning, workforce development, and targeted investments to enhance the security and resilience of the transportation network, while highlighting Texas as a growing hub for TCI activity.

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.

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

TL;DR

This study addresses how three transportation cybersecurity–related industries (cybersecurity, automotive, and transportation and logistics) spatially cluster and how visitor flows evolve across the United States. It introduces BiTransGCN, a hybrid Transformer–GCN model, to forecast long-term, non-Euclidean spatiotemporal visitor flows by integrating SafeGraph origin–destination data with American Community Survey socioeconomics and NAICS industry categories. Through Global Moran’s I, GeoShapley, and SHAP-based analyses, the work links geolocation, education, and other social factors to dynamic clustering patterns and predicts a US-wide average visitor-flow increase of , with industry-specific trajectories. The findings offer actionable insights for economic planning, workforce development, and targeted investments to enhance the security and resilience of the transportation network, while highlighting Texas as a growing hub for TCI activity.

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.
Paper Structure (16 sections, 12 equations, 13 figures, 3 tables)

This paper contains 16 sections, 12 equations, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Industry Spatiotemporal Clustering Pattern
  4. Deep Learning-Based Long-Term Flow Prediction
  5. Data and Study Area
  6. Data Processing
  7. Research Process
  8. Methodology
  9. Long-term Flow Prediction Based on Deep Learning
  10. Geospatial Factor Importance Analysis Method
  11. Results and Discussion
  12. Spatiotemporal Changes of Visitor Flows in TCI-related Industries
  13. Spatial Clustering Feature Analysis
  14. Prediction of Visitor Flow Changes of TCI-related Industries
  15. Spatial Effects and Features Contribution Analysis of Socioeconomic Factors
  16. ...and 1 more sections

Figures (13)

  • Figure 1: Geographic coding and CGB density maps
  • Figure 2: Research framework chart
  • Figure 3: The structure of BiTransGCN model applied for long-term flow prediction
  • Figure 4: a) Scaled dot-product attention and multi-head self-attention and b) the architecture of transformer
  • Figure 5: The structure of GCN with multiple graph convolutional layers
  • ...and 8 more figures