An Approach to Resilience in Transportation Planning

An Approach to Resilience in Transportation Planning

I.M. Schoeman

Unit for Business Mathematics and Informatics, North West University (Potchefstroom Campus), South Africa

Page: 
1061-1071
|
DOI: 
https://doi.org/10.2495/SDP-V13-N8-1061-1071
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
12 December 2018
| Citation

OPEN ACCESS

Abstract: 

Dynamic systems are characterize by a collection of variables and their interrelationships over time. Hence, a dynamic system refers to anything that evolves or changes over time like your bank account or a countries economic growth. Resilience is the ability of a dynamic system to return to a steady state or stable periodic orbit after a (not to big) disturbance and therefore the systems behaviour will be stable or marginally stable. Resilience behaviour of the system strives to minimize regret and mitigate risk by being a stable or marginally stable system. in mathematical terms this definition of resilience refer to convergence, for all starting values near the equilibrium (or small disturbance to the equilibrium) the system will not move away from the/a equilibrium, i.e. a stable (steady state) system or the stable periodic orbit. hence the system will oscillate between a finite number of points on the long term.

To achieve sustainability within a system (e.g. transportation networks, etc.), the way of thinking, planning, project design and implementation needs to be resilient in order to contribute to the system wide sustainability. It thus implies a need for quantitative data and information to optimise planning and to support decision making in an adaptive fashion. Through being able to describe how the system evolves over time, it enable ways to define preconditions or input that will ensure a dynamic system remain stable to promote resilience and sustainability. At present, theory and practices do not make provision for the development of improved adaptive capacities in all phases of planning through dynamic transportation systems planning and development.

The aim of this paper is to introduce and develop resilience-orientated frameworks and approaches based on application of mathematical, statistical and decision-making tools, which can be used to enhance the interface between resilience and sustainability alignment though dynamic thinking, planning and implementation in transportation systems. in the end, this will lead to improved management and sustainable transport planning.

Keywords: 

adaptive capacity and decision making, dynamic planning, dynamic transportation systems, resilience, sustainability

  References

[1] Derissen, S., Quaas, M.F. & Baumgärtner, S., The relationship between resilience and sustainability of ecological-economic systems. Ecological Economics, 70(6), 1121– 1128, 2011. https://doi.org/10.1016/j.ecolecon.2011.01.003.

[2] Schoeman, C.B., Theoretical perspectives on resilience and sustainability in transportation and spatial planning. WIT SPD Conference, Bristol, 2017.

[3] Schjetlein, T., Haavaldsen, T. & Lohne, J., Achieving sustainability? A case analysis of policy-to-project processes. Procedia – Social and Behavioral Sciences, 226, pp. 140– 147, 2016. https://doi.org/10.1016/j.sbspro.2016.06.172

[4] Berdica, K., An introduction to road vulnerability: What has been done, is done and should be done. Transport Policy, 9(2), pp. 117–127, 2002. https://doi.org/10.1016/ s0967-070x(02)00011-2

[5] Anderies, J.M., Folke, C., Walker, B. & Ostrom, E., Aligning key concepts for global change policy: Robustness, resilience, and sustainability. Ecology and Society, 18(2), p. 8, 2013. http://dx.doi.org/10.5751/ES-05178-180208 [Crossref], [Web of Science ®], [Google Scholar]

[6] Scheinerman, E.R., Invitation to Dynamical Systems. ISBN: 0-13-185000-8.

[7] Hull, J.C., Options, Futures and Other Derivatives, Pearson, 1993. ISBN: 0-13-149908-4.

[8] Rubinstein, R.R., Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks. Wiley: New York, 1986.

[9] Anderson, G.A., Hoffman, D.L. & Rasche, R.H., A Vector Error Correction forecasting model of the U.S. economy, pp. 569–598, 2002.

[10] Asteriou, D. & Hall, S.G., Applied Econometrics, 3rd edn., Published by Palgrave, 2016.

[11] Gujarati, D.N. & Porter, D.C., Basic Econometrics, 5th edn., Published by McGrawHill Irwin, 2009.

[12] Chu, P.K., Relationship Between Macroeconomic Variables and Net Asset Values (NAV) of Equity Funds: Cointegration Evidence and Vector Error Correction Model of the Hong Kong Mandatory Provident Funds (MPFs), pp. 792–810, 2011.

[13] Oriavwote, V.E. & Eriemo, N.O., Oil prices and the real exchange rate in Nigeria, pp. 198–205, 2012.

[14] Schoeman I.M., Strategies to reduce traffic accident rates in developing countries: ­Lessons learned for assessment and management. International Journal of Safety and Security Engineering, 8(1), pp. 98–109, WIT Press, ISSN: 2041-9031, 2018.

[15] Heij, C., Ran, A. & Van Schagen, F., Introduction to Mathematical Systems Theory: Linear Systems, Identification and Control. ISBN: 13: 978-3-7643-7548-5.

[16] Curtain, R.F. & Zwart, H.J., An introduction to Infinite-Dimensional Linear Systems Theory, Springer Verlag, 1995.

[17] O’Sullivan, A., Urban Economics, pp. 266–269, McGraw Hill, 2009. ISBN: 978-007-127629-0.

[18] Bottle, M., Di Salvo, C., Placido, A., Motella, B. & D’ Acierno, L., A neighbourhood search algorithm for determining optimal intervention strategies in the case of metro system failures. International Journal of Transport Development and Integration, 1(1), pp. 63–73, 2017. https://doi.org/10.2495/tdi-v1-n1-63-73

[19] Fletcher, R., Practical Methods of Optimization, Wiley: New York, 1987. [20] Peressini, A.L., Sullivan, F.E. & Uhl, J.J., The Mathematics of Nonlinear Programming, Springer-Verlag: New York, 1988.