Evaluation of Risk and Resilience of the MBTA Green Rapid Transit System

TL;DR

This paper assesses the risk and resilience of the MBTA Green Line using graph theory, network metrics, and scenario analysis to identify vulnerabilities and suggest improvements for cyber-physical threat mitigation.

Contribution

It introduces a network-based risk assessment framework for the Green Line, combining graph theory, the MBRA tool, and scenario analysis to identify critical vulnerabilities.

Findings

North Station, Government Center, Haymarket, Copley, and Kenmore are most critical nodes.

Cyber-physical attack scenarios reveal key vulnerabilities.

Budget allocation impacts threat reduction effectiveness.

Abstract

The Transportation Systems Sector is one of the sixteen critical infrastructure sectors identified by the Cybersecurity and Infrastructure Security Agency (CISA) and plays a crucial role in ensuring public safety, economic stability, and national security. The Massachusetts Bay Transportation Authority (MBTA) serves as the primary public transportation system in the Greater Boston Area, with the Green Line representing one of the oldest and most complex rapid transit systems in the network. This paper presents a network-based risk and resilience assessment of the MBTA Green Line using graph theory, network metrics, and the Model-Based Risk Analysis (MBRA) tool. The original 70-station Green Line network is simplified into a 17-node model, and key metrics, including degree centrality, betweenness centrality, eigenvector centrality, spectral radius, node robustness, and blocking nodes, are computed using Python-based analysis. Critical vulnerability is derived using the MBRA resiliency equation, and random, targeted, and cyber-physical attack scenarios are evaluated. The results identify North Station, Government Center, Haymarket, Copley, and Kenmore as the most critical nodes. A fault tree analysis between Kenmore and Copley further demonstrates the impact of budget allocation on threat reduction. This work highlights key vulnerabilities in the Green Line network and provides actionable recommendations to improve resilience against cyber-physical threats.