Civil infrastructure resilience: state-of-the-art on transportation network systems

ABSTRACT

This paper presents a synthesis of the state-of-the-art on transportation network resilience. The socio-technical approach associated with resilience evaluation is broad and multidisciplinary, focusing on the network’s ability to sustain functionality and recover speedily after disruptions. The three key problem areas identified in literature were: minimal network-level study applications of resilience; insufficient practical methods in quantifying the recovery phase of resilience; and the need for the development of resilience indexes demonstrated on real-life regional network models. The authors of this paper recommend that: further investigative efforts are directed towards the post-disaster phases of resilience; evaluating the applicability of resilience indexes on multiple hazard events for transportation networks is requisite; and the formulation of resilience indexes based on regional network models and variable demand traffic assignment models. Furthermore, collaborative efforts between management authorities and researchers are necessary to facilitate the advancement and enactment of necessary policies to enhance transportation systems’ resilience.

KEYWORDS:

• Resilience
• transportation
• networks
• civil infrastructure
• network performance

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Adaptive capacity was defined as the ability of the system to allocate resources in response to a disruption, with higher adaptive capacity indicative of the system’s ability to withstand higher shocks. (Gunderson et al. 2002).

2. In a User Equilibrium traffic assignment, “paths connecting any O-D pair are divided into two categories, i.e. those carrying flow, on which travel time equals the minimum O-D travel time; and those not carrying flow, on which travel time is greater than (or equal to) the minimum O-D travel time.” (Daskin and Sheffi 1985).

3. System Optimum traffic assignment minimizes total travel time spent in the network while satisfying flow conservation constraints (i.e. all O-D trip rates are assigned to the network). (Daskin and Sheffi 1985).

4. The theory of belief functions provides a non-Bayesian way of using mathematical probability to quantify subjective judgements by assessing probabilities for related questions and considering implications of these probabilities for the question of interest. (Shafer 1976).