Feasibility risk assessments of transport projects using Monte Carlo-simulations
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The idea of using reference classes is to gather all possible information regarding already implemented infrastructure investment projects, which thus can be used to provide a background for evaluation of future projects and identify probability distributions that can model the uncertainty of the project impacts. This includes impacts as e.g., travel time savings and construction cost <ref name=''Modelling''> ''Leleur, S., Salling, B., 2012. Modelling of Transport Project Uncertainties: Feasibility Risk Assessment and Scenario Analysis. Department of Transport, Technical University of Denmark'' </ref>. | The idea of using reference classes is to gather all possible information regarding already implemented infrastructure investment projects, which thus can be used to provide a background for evaluation of future projects and identify probability distributions that can model the uncertainty of the project impacts. This includes impacts as e.g., travel time savings and construction cost <ref name=''Modelling''> ''Leleur, S., Salling, B., 2012. Modelling of Transport Project Uncertainties: Feasibility Risk Assessment and Scenario Analysis. Department of Transport, Technical University of Denmark'' </ref>. | ||
The result of the RSF using Monte Carlo simulation is a certainty graph and/ or certainty interval providing information about the certainty of achieving e.g., a BCR of at least one. That is potentially valuable information for decision makers describing in how many cases or how large the certainty is of obtaining a project that is feasible. | The result of the RSF using Monte Carlo simulation is a certainty graph and/ or certainty interval providing information about the certainty of achieving e.g., a BCR of at least one. That is potentially valuable information for decision makers describing in how many cases or how large the certainty is of obtaining a project that is feasible. | ||
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| + | == Reasoning behind == | ||
| + | === Optimism bias === | ||
| + | == Application == | ||
| + | === Historical data === | ||
| + | === Monte Carlo simulation === | ||
| + | ==== Choosing distribution ==== | ||
| + | ==== Certainty graph ==== | ||
| + | == Limitations == | ||
| + | == Other application areas == | ||
== References == | == References == | ||
<references /> | <references /> | ||
Revision as of 18:10, 12 February 2023
Contents |
Abstract
A common way of assessing the feasibility of an infrastructure projects (e.g., road or rail) is through a cost benefit analysis (CBA) , resulting in an overview of the cost and benefits/ consumer surplus as the Net present value (NPV) or Benefit cost ratio (BCR) of the project. Values which can be compared to other projects/alternatives. The result of a CBA though relies on a single value where all assumptions, estimations and calculations are reduced to a single number. It can thus be difficult to describe the viability of the result and due to the complexity of infrastructure projects a risk of cost overruns and overestimations of benefits are historically likely to occur [1]. One way to challenge this problem is to perform a Feasibility risk assessment (FRA) handling the uncertainties present in the decision making.
The reasoning of doing a feasibility risk assessment is to move from a single point result to an interval result providing additional knowledge on the viability of a project. One FRA technique trying to cope with some of the shortcomings of CBA are a stochastic approach based on Monte Carlo simulations using reference classes. Also called reference scenario forecasting (RSF). The idea of using reference classes is to gather all possible information regarding already implemented infrastructure investment projects, which thus can be used to provide a background for evaluation of future projects and identify probability distributions that can model the uncertainty of the project impacts. This includes impacts as e.g., travel time savings and construction cost [2]. The result of the RSF using Monte Carlo simulation is a certainty graph and/ or certainty interval providing information about the certainty of achieving e.g., a BCR of at least one. That is potentially valuable information for decision makers describing in how many cases or how large the certainty is of obtaining a project that is feasible.
