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

arXiv 2505.08822 ↗First posted 12 May 2025cs.CY cs.LG physics.soc-phcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 61 references that are in the map. First posted on arXiv 12 May 2025.

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References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  6. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  7. 2016Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning frameworkcited by 23
  8. 2022Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representationscited by 21
  9. 2021Joint Shapley values: a measure of joint feature importancecited by 15
  10. 2022STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graphcited by 12
  11. 2023GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Modelscited by 5
  12. 2025Cybersecurity in Transportation Systems: Policies and Technology Directionscited by 1

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