Practice-based post-disaster road network connectivity analysis using a data-driven percolation theory-based method

Kuo-Hao Chang, Jiuh-Biing Sheu, Yenming J. Chen, Chieh-Hsin Chang and Chih-Hao Liu

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 171, issue C

Abstract: This paper proposes a data-driven percolation theory-based approach that analyzes post-disaster traffic network connectivity in the case of earthquakes. The proposed approach’s main contribution is integrating real disaster-related data and percolation theory to analyze scenario-based post-disaster traffic network connectivity. The proposed approach adopts a data-driven estimation function to associate the link-based data, including the permanent ground deformation provided by a government-sponsored disaster-related information platform. This study validates the feasibility of the proposed approach through an empirical study. We find that network connectivity decreases with a similar slope in the scenarios of earthquakes with magnitudes between 5.0 and 6.5 but begins to flatten at larger magnitudes. In addition, most of the isolated areas are located in mountainous and coastal areas.

Keywords: Post-disaster road network; Connectivity analysis; Percolation theory; Road bottleneck; Isolated area (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.tre.2023.103022

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