Towards improving resilience of cities: an optimisation approach to minimising vulnerability to disruption due to natural disasters under budgetary constraints

Allan Peñafiel Mera and Chandra Balijepalli ()
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Allan Peñafiel Mera: University of Leeds
Chandra Balijepalli: University of Leeds

Transportation, 2020, vol. 47, issue 4, No 10, 1809-1842

Abstract: Abstract In recent years, climate change emerged as a dominant concern to many parts of the world bringing in huge economic losses disturbing normal business/life. In particular cities are suffering from floods affecting land based transportation systems in a significant manner more frequently than ever. Many local authorities facing funding cuts are suffering from limited budgets and they are put under even higher pressure when looking for resources to recover the damaged networks. The agencies involved with post-disaster reconstruction too struggle to prioritise the network links to recover. This paper addresses the problem of road maintenance/development with the aim of improving resilience of the network by formulating the problem as a mathematical model that minimises the vulnerability to disruption due to natural incidents under budgetary constraints. This paper extends the critical link analysis from a single link being disrupted to the case of multiple links, and for the first time proposes an objective function involving a measure of vulnerability to minimise. Metaheuristic Simulated Annealing method is used to reach near global optimal solution for a real-life network with large demand. A segment of the City of York in England has been used to illustrate the principles involved. Numerical experiments indicate that Simulated Annealing based optimisation method outperforms the ‘volume-priority’ heuristic approach, returning higher value for money spent. The proposed approach spreads the benefits across wider population by including more number of links in the priority list while reducing the vulnerability to disruption.

Keywords: Resilience; Vulnerability; Bi-level optimisation; Simulated Annealing; Traffic assignment (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11116-019-09984-8

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