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Transport appraisal and Monte Carlo simulation by use of the CBA-DK model

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  • Salling, Kim Bang
  • Leleur, Steen

Abstract

This paper presents the Danish CBA-DK software model for assessment of transport infrastructure projects. The assessment model is based on both a deterministic calculation following the cost-benefit analysis (CBA) methodology in a Danish manual from the Ministry of Transport and on a stochastic calculation, where risk analysis is carried out using Monte Carlo simulation. Special emphasis has been placed on the separation between inherent randomness in the modeling system and lack of knowledge. These two concepts have been defined in terms of variability (ontological uncertainty) and uncertainty (epistemic uncertainty). After a short introduction to deterministic calculation resulting in some evaluation criteria a more comprehensive evaluation of the stochastic calculation is made. Especially, the risk analysis part of CBA-DK, with considerations about which probability distributions should be used, is explained. Furthermore, comprehensive assessments based on the set of distributions are made and implemented by use of a Danish case example. Finally, conclusions and a perspective are presented.

Suggested Citation

  • Salling, Kim Bang & Leleur, Steen, 2011. "Transport appraisal and Monte Carlo simulation by use of the CBA-DK model," Transport Policy, Elsevier, vol. 18(1), pages 236-245, January.
  • Handle: RePEc:eee:trapol:v:18:y:2011:i:1:p:236-245
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    References listed on IDEAS

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    1. Rothengatter, Werner, 2003. "How good is first best? Marginal cost and other pricing principles for user charging in transport," Transport Policy, Elsevier, vol. 10(2), pages 121-130, April.
    2. Salling, Kim Bang & Banister, David, 2009. "Assessment of large transport infrastructure projects: The CBA-DK model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(9-10), pages 800-813, November.
    3. Mackie, Peter & Preston, John, 1998. "Twenty-one sources of error and bias in transport project appraisal," Transport Policy, Elsevier, vol. 5(1), pages 1-7, January.
    4. Bruzelius, Nils & Flyvbjerg, Bent & Rothengatter, Werner, 2002. "Big decisions, big risks. Improving accountability in mega projects," Transport Policy, Elsevier, vol. 9(2), pages 143-154, April.
    5. Short, Jack & Kopp, Andreas, 2005. "Transport infrastructure: Investment and planning. Policy and research aspects," Transport Policy, Elsevier, vol. 12(4), pages 360-367, July.
    6. Rodier, Caroline J. & Johnston, Robert A., 2002. "Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(7), pages 613-631, August.
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    Cited by:

    1. Philipp Kehrein & Mark van Loosdrecht & Patricia Osseweijer & John Posada & Jo Dewulf, 2020. "The SPPD-WRF Framework: A Novel and Holistic Methodology for Strategical Planning and Process Design of Water Resource Factories," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-31, May.
    2. Florio, Massimo & Forte, Stefano & Sirtori, Emanuela, 2016. "Forecasting the socio-economic impact of the Large Hadron Collider: A cost–benefit analysis to 2025 and beyond," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 38-53.
    3. Salling, Kim Bang & Leleur, Steen, 2015. "Accounting for the inaccuracies in demand forecasts and construction cost estimations in transport project evaluation," Transport Policy, Elsevier, vol. 38(C), pages 8-18.
    4. Chou, Jui-Sheng & Ongkowijoyo, Citra Satria, 2015. "Reliability-based decision making for selection of ready-mix concrete supply using stochastic superiority and inferiority ranking method," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 29-39.

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