Pre-disaster resource allocation based on network topology and flow features

Abstract

Protecting transportation systems from severe damage is crucial for their post-disaster functionality. We adopt network topological metrics and generalised topological metrics involving demand and traffic flow characteristics to identify critical links for enhancing post-event transportation network performance. A single-parameter, non-monotonic function is proposed to allocate limited resources based on these metrics. Monte Carlo simulation generates failure scenarios to evaluate residual network performance through network topological measures and network level of service indicators. This work introduces ‘Trip Efficiency’, a novel level of service indicator that considers both demand connectivity and travel efficiency for a network with multiple links interrupted. In Anaheim case study network, betweenness-based resource allocation leads to better performance on network topological measures; while generalised topological metrics result in more connected OD pairs and higher travel efficiency. The experimental results demonstrate the effectiveness of the proposed method in enhancing network resilience against multisite disruptive events through proactive resource allocations.

KEYWORDS:

• Road transportation system
• complex network theory
• generalised topological metrics
• traffic assignment
• resilience

Acknowledgements

This research was supported by the Fundamental Research Funds for the Central Universities 2022RC019; and the National Natural Science Foundation of China (Nos. 72201028, 72288101, 72242102, 72091513). The views, opinions, findings, and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of Beijing Jiaotong University, or any other entity.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Fundamental Research Funds for the Central Universities 2022RC019; and the National Natural Science Foundation of China (Nos. 72201028, 72288101, 72242102, 72091513).