01658670

Component

The ability to detect critical spots in transportation networks is fundamental to improve traffic operations and road-network resilience. Real-time monitoring of these networks, especially in very large metropolitan areas, is a compelling challenge due to the complexity of computing robustness metrics. This paper presents a study of vulnerability in a real-world, very-large road network by adopting graph-based modeling and analysis, and big-data techniques for processing the related datasets. The authors first analyze the correlation between global efficiency and betweenness centrality, proving that nodes with higher betweenness centrality influence network vulnerability the most. Then, the authors present an algorithm for fast computation of approximated betweenness centrality that significantly reduces execution time. The evaluation shows that the approximation error does not significantly affect the most critical nodes, thus making the algorithm well-suited for on-line operational monitoring of road networks vulnerability

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

18-04089

English

17p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References

Algorithms; Data analysis; Disaster resilience; Highways; Metropolitan areas; Network analysis (Planning); Network nodes; Risk assessment

Data and Information Technology; Highways; Planning and Forecasting; Security and Emergencies

Transportation Research Board Annual Meeting 2018 Paper #18-04089

TRIS, TRB, ATRI

Jan 8 2018 11:00AM