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Comprehensive resilience analysis in transportation

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  • Full or part time
    Dr E Heinen
    Dr J Wang
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Lead supervisor: Dr Wang. Co-supervisor: Dr Heinen


Resilience is a trendy word but unlike sustainability, it does not have a unified definition. Resilience is universally considered to be important and in some ways, e.g. in planning strategies for climate adaptation, it appears that resilience is replacing sustainability (Davoudi, 2012). Some aspects of resilience have received considerable attention in transport planning for over a decade. Yet, it is not clear what resilience means in transport planning.

Resilience is a concept in ecology first introduced by Holling as “a measure of the persis- tence of systems and their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables” (Holling, 1973, p.14).

Transportation systems are similar to ecological systems, in the sense that they are both complex, adaptive and self-organising. By applying the concepts of resilience in ecology, Wang (2015) defined “comprehensive resilience in transportation” as the quality that leads to “recovery, reliability and sustainability”.

From a literature review, it appears that resilience in transportation by default is associated with engineering resilience, i.e. resilience analysis in transportation in the literature has mostly covered recovery and reliability. Focussing only on engineering resilience imposes the danger of reducing the resilience of the system in other aspects, such as human health as part of adaptive capacity of the system and transformability. Therefore, we must incorporate transformability in our analysis framework in order to achieve “comprehensive resilience in transportation” (Wang, 2015).

Applying this concept in transport planning, let us consider, for example, the majority of the population driving to work is considered to be normal. Now we imagine a worst case scenario that fossil fuels have run out, then for those who normally drive to work, life might never be able to get back to normal again. On the contrary, for those who normally bike or walk to work, life would remain normal. For those who take public transport, life might or might not have changed, depending on the sources of energy for the public transport system. Obviously, a multi-modal transportation system with diversity, i.e. a wide variety of mode and route choices, would be a more resilient system as compared with a car-dominated system. If everyone owns a bike and is fit enough to ride, then even when there is no petrol, the system can still function, at least partially, because it can be easily transformed to a bike-and-walk system.

In ecology, transformability becomes very important when a system is in a stability regime that is considered undesirable, and it is either impossible, or getting progressively harder and harder, to engineer a ‘flip’ to the original or some other stability regime of that same system (McDonald and Walker, 2007, p.86). In transport planning, “transformability” is central to “comprehensive resilience in transportation”, which is a perspective that has been overlooked in resilience analysis (Wang, 2015).

Aims and Approach

To support comprehensive resilience analysis, this study aims to develop modelling techniques to assess "comprehensive resilience" of multi-modal transport networks including performance measures of important aspects such as "transformability".

A multi-objective optimisation approach will be applied in a bilevel modelling framework. For a comprehensive resilience analysis, naturally we must consider multiple objectives at both levels. The lower level represents users’ decisions such as residential location and transport mode choices, while the upper level represents policy makers’ decisions with multiple objectives, including comprehensive resilience criteria to measure the system performance of the transportation system in terms of resilience as a result of users’ behavioural change. Here are some examples of some recent work in applying multi-objective optimisation to model travel choices, which can be applied to support informed policy decisions in transport planning, see e.g. Ehrgott et al. (2015); Wang and Ehrgott (2013); Wang et al. (2014a,b).

With this approach, we will be able to identify the boundaries in the system performance measures for the economic, environmental, social and resilience objectives. Optimisation techniques such as Data Envelopment Analysis (DEA) will be applied in the evaluation process to identify the efficient frontier in multiple dimensions. This will enable decision makers to trade-off between different objectives and hence make fully informed policy decisions.

Please visit our LARS scholarship page for more information and further opportunities:

Funding Notes

Entry Requirements/Necessary Background:
Background in Operational Research, Mathematics, Economics.


Davoudi, S. (2012). Resilience: A bridging concept or a dead end? Planning Theory and Practice, 13(2), 299–307.

Ehrgott, M., Wang, J. Y. T., and Watling, D. P. (2015). On multi-objective stochastic user equilibrium. Transportation Research Part B, 81(3), 704–717.

Grant-Muller, S., Mackie, P., Nellthorp, J., and Pearman, A. (2001). Economic appraisal of european transport projects: The state-of-the-art revisited. Transport Reviews, 21(2), 237–261.

Holling, C. S. (1973). Resilience and stability for ecological systems. Annual Review of Ecology and Systematics, 4, 1–23.

McDonald, T. and Walker, B. (2007). Resilience thinking: Interview with Brian Walker. Ecological Management and Restoration, 8(2), 85–91.

Wang, J. Y. T. (2015). ‘Resilience thinking’ in transport planning. Civil Engineering and Envi- ronmental Systems, 32(1-2), 180–191.

Wang, J. Y. T. and Ehrgott, M. (2013). Modelling route choice behaviour in a tolled road network with a time surplus maximisation bi-objective user equilibrium model. Transportation Research Part B: Methodological, 57, 342–360.

Wang, J. Y. T., Ehrgott, M., and Chen, A. (2014a). A bi-objective user equilibrium model of travel time reliability in a road network. Transportation Research Part B: Methodological, 66, 4–15.

Wang, J.Y.T., Ehrgott, M., Dirks, K.N. and Gupta, A. (2014b). A bilevel multi- objective road pricing model for economic, environmental and health sustain- ability, in: The Seventeenth Meeting of the EURO Working Group on Transportation (EWGT2014), July 2014, Seville, Spain, Elsevier. pp. 393–402.

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