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Assessment of large transport infrastructure projects : the CBA-DK model

Abstract:
This paper presents a newly developed decision support model to assess transport infrastructure projects: CBA-DK. The model combines use of conventional cost–benefit analysis to produce aggregated single point estimates, with quantitative risk analysis using Monte Carlo simulation to produce interval results. The embedded uncertainties within traditional CBA such as ex-ante based investment costs and travel time savings are of particular concern. The paper investigates these two impacts in terms of the Optimism Bias principle which is used to take account of the underestimation of construction costs and the overestimation of travel time savings. The CBA-DK methodological approach has been used to apply suitable probability distribution functions on the uncertain parameters, thus resulting in feasibility risk assessment moving from point to interval results. The proposed assessment model makes use of both deterministic and stochastic based information. Decision support as illustrated in this paper aims to provide assistance in the development and ultimately the choice of action, while accounting for the uncertainties surrounding transport appraisal schemes. The modelling framework is illustrated by the use of a case study appraising airport and runway alternatives in the capital of Greenland – Nuuk. The case study has been conducted in cooperation with the Home Rule Authorities of Greenland.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.tra.2009.08.001

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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Transport Studies Unit
Role:
Author


Publisher:
Elsevier
Journal:
Transportation Research Part A: Policy and Practice More from this journal
Volume:
43
Pages:
800-813
Publication date:
2009-01-01
DOI:
ISSN:
0965-8564


Language:
English
Keywords:
Subjects:
UUID:
uuid:be3738b3-d3e2-4bee-818b-5caf8a0accc3
Local pid:
daisy:2153
Source identifiers:
2153
Deposit date:
2012-08-07
ARK identifier:

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