Traffic and Transportation Journal

Article

Comprehensive Sensitivity Analysis of Cost-Benefit Analysis Variables for Transport Interventions

Mattias JUHÁSZ, Tamás MÁTRAI

DOI:https://doi.org/10.7307/ptt.v36i4.529

Keywords:sensitivity analysis, cost-benefit analysis, transport appraisal

Abstract

Cost-benefit analysis (CBA) is the universally applied tool to assess economic viability in assisting decisions on transport investments. Its framework is heavily influenced by the numerous variables it considers through estimating and valuing the intervention’s effects. This paper – utilising the authors’ previously implemented CBA test environment – comprehensively analyses the sensitivity of significant variables of three typical CBA models for transport interventions (road, rail and urban) to understand the prevailing appraisal approach better and to help focus on further methodological improvements. Morris and Sobol methods were selected to study the global sensitivity of and the relations between the input parameters of the models. The sensitivity test of the three analysed models provided similar results regarding which variables are most influential in CBAs. Input variables such as the investment cost, the economic discount rate, forecasted GDP changes and specific elasticities to these GDP changes often have a firm but mostly linear effect. Value of time, vehicle operating cost and mode choice-related parameters such as car availability, car occupancy rate, level of service indicators (e.g. frequency of service) and potential to induce travel demand (proxied by a ‘no travel’ parameter) are inputs with considerable linear effects and greater interactive effects.

References

[1] Laird J, Nash C, Mackie P. Transformational transport infrastructure: Cost-benefit analysis challenges. Town Planning Review. 2014;85(6):709-730. DOI: 10.3828/tpr.2014.43.
[2] Mackie P, Worsley T, Eliasson J. Transport appraisal revisited. Research in Transportation Economics. 2014;47:3-18. DOI: 10.1016/j.retrec.2014.09.013.
[3] van Wee B, Börjesson M. How to make CBA more suitable for evaluating cycling policies. Transport Policy. 2014;44:117-124. DOI: 10.1016/j.tranpol.2015.07.005.
[4] Flyvbjerg B. Cost overruns and demand shortfalls in urban rail and other infrastructure. Transportation Planning and Technology. 2007;30(1):9-30. DOI: 10.1080/03081060701207938.
[5] Borjesson M, Eliasson J, Lundberg M. Is CBA ranking of transport investments robust? Journal of Transport Economics and Policy. 2014;48(2):189-204. https://www.jstor.org/stable/24396325.
[6] de Jong G, et al. Uncertainty in traffic forecasts: Literature review and new results for The Netherlands. Transportation. 2007;34:375-395. DOI: 10.1007/s11116-006-9110-8.
[7] O’Mahony T. Cost-benefit analysis and the environment: The time horizon is of the essence. Environmental Impact Assessment Review. 2021;89:106587. DOI: 10.1016/j.eiar.2021.106587.
[8] Eliasson J, Börjesson M, Odeck J, Welde M. Does benefit–cost efficiency influence transport investment decisions? Journal of Transport Economics and Policy (JTEP). 2015;49(3):377-396. https://www.jstor.org/stable/jtranseconpoli.49.3.0377.
[9] Holz-Rau C, Scheiner J. Safety and travel time in cost-benefit analysis: A sensitivity analysis for North Rhine-Westphalia. Transport Policy. 2011;18(2):336–346. DOI: 10.1016/j.tranpol.2010.10.001.
[10] Eliasson J, Fosgerau M. Cost overruns and demand shortfalls – Deception or selection? Transportation Research Part B: Methodological. 2013;57:105-113. DOI: 10.1016/j.trb.2013.09.005.
[11] Juhász M, Mátrai T, da Cruz JHO, Török Á. Test environments to analyse methodological improvements of costbenefit analysis for transport interventions. Periodica Polytechnica Transportation Engineering. 2023;51(2):155-165. DOI: 10.3311/PPtr.20996.
[12] TRENECON. Módszertani útmutató egyes közlekedési projektek költség-haszon elemzéséhez. Nemzeti Fejlesztési Minisztérium. 2016.
[13] Sartori D, et al. Guide to cost-benefit analysis of investment projects: Economic appraisal tool for cohesion policy 2014 – 2020. European Commission. 2014.
[14] Saltelli A, et al. Global sensitivity analysis: The primer. Chichester, UK. John Wiley and Sons Ltd, 2008.
[15] Morris MD. Factorial sampling plans for preliminary computational experiments. Technometrics. 1991;33(2):161–174. DOI: 10.2307/1269043.
[16] Campolongo F, Cariboni J, Saltelli A. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, Modelling, Computer-Assisted Simulations, and Mapping of Dangerous Phenomena for Hazard Assessment. 2007; 22(10):1509-1518. DOI: 10.1016/j.envsoft.2006.10.004.
[17] Homma T, Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering & System Safety. 1996;52(1):1-17. DOI: 10.1016/0951-8320(96)00002-6
[18] Sobol IM. On sensitivity estimation for nonlinear mathematical models. Matematicheskoe Modelirovanie. 1990;2:112–118.
[19] Martinez J-M. Analyse de sensibilite globale par decomposition de la variance. Presentation in “Journée des GdR Ondes & Mascot Num”. Institut Henri Poincaré, Paris, France. 2011.
[20] Odeck J, Kjerkreit A. The accuracy of benefit-cost analyses (BCAs) in transportation: An ex-post evaluation of road projects. Transportation Research Part A: Policy and Practice. 2019;120:277–294. DOI: 10.1016/j.tra.2018.12.023.
[21] Hörcher D, Graham DJ, Anderson RJ. Crowding cost estimation with large scale smart card and vehicle location data. Transportation Research Part B: Methodological. 2017;95:105-125. DOI: 10.1016/j.trb.2016.10.015.

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Published

27.08.2024

Issue

2024 (Vol 36), Issue 4

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