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Identifying the Factors Driving the Uncertainty in Transport Infrastructure Project by Application of Structural Dynamic Analysis to a Backcast Scenario

Lookup NU author(s): Pete Sykes, Professor Margaret Carol Bell CBE, Dr Dilum Dissanayake

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

Transport planning, in theory, is underpinned by rational analysis of the benefits of proposed developments. However,project outcomes do not always follow the results of that analysis and uncertainty is evident during the decision makingprocesses. This research has devised and demonstrated a method to analyse that uncertainty, focussing on the earlystages of the project lifecycle. Stakeholders were interviewed to elicit their opinions about a normative scenario andthese interviews coded using qualitative data analysis techniques. The emerging variables were analysed, using astructural dynamic model, based in complexity theory, which develops measures of connectivity to classify variables bytheir roles in inception and uncertainty in the project. The case study was based on a disused railway withcontradictory views on the benefits of reopening it. In the normative scenario, the rail service is re-instated inconjunction with a new sustainable urban development. The findings from this case study were that executiveleadership and collaboration between Local Authorities were the most influential determinants for progress, and thatthe prime causes of uncertainty were the extant economic and planning policies. During the course of the project,structural governance developments have occurred in the UK that have endorsed these findings.


Publication metadata

Author(s): Sykes P, Bell MC, Dissanayake D

Publication type: Article

Publication status: Published

Journal: Futures

Year: 2019

Volume: 111

Pages: 26-41

Print publication date: 01/08/2019

Online publication date: 18/05/2019

Acceptance date: 14/05/2019

Date deposited: 25/07/2019

ISSN (print): 0016-3287

ISSN (electronic): 1873-6378

Publisher: Pergamon Press

URL: https://doi.org/10.1016/j.futures.2019.05.003

DOI: 10.1016/j.futures.2019.05.003


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