Successfully completing a business case requires establishing a robust baseline – there are three elements to consider:
- the base case
- assumptions underlying the demand forecasts
- treatment of risk and uncertainty
The Base Case
Project evaluations compare the costs and benefits of doing something, or the project case, with not doing it, or the base case. The base case and project case enable a comparison for calculating costs of capital investments and resulting outcomes or benefits.
So establishing a robust base case as the baseline or foundation for the comparison is key to building an effective business case. An incorrectly specified base case can bias the analysis of different options by overstating benefits or understating costs.
The base case should be based on a ‘business-as-usual’ or ‘do-minimum’ approach, assuming the continued safe operation or a transport network or service. This will include committed and funded expenditure. Note that ‘do-minimum’ is not the same as ‘do-nothing’ as this is not likely to be the reality.
A ‘do-minimum’ base case assumes that general operating, routine and periodic maintenance costs will continue to occur, plus a minimum level of capital expenditure to maintain services at or near their current service level and should not include asset upgrades.
If a thorough investigation of do-minimum options shows there are no minimum interventions available because they have all been implemented, then the do-nothing option may be the only possible Base Case.
Further reading: ATAP. 2018. T2 Cost Benefit Analysis
Assumptions
Estimating future demand for a transport service, usually involves using a forecasting model – a tool for understanding and assessing the likely impacts of changes in the drivers of the service, such as transport supply, demographics or land use.
The role of transport models is to provide structured forecasts that can be interrogated to provide information on implications of transport interventions.
Transport models are a systematic representation of the complex real-world transport and land use system as it exists. They are powerful tools for assessing the impacts of transport infrastructure options and for identifying how the transport system is likely to perform in future.
Note that a model is an estimate, a simplification of the real world and is based on certain assumptions. New infrastructure can have a useful life of 20 to 100+ years, however models can only really predict with reasonable accuracy the next 10 to 20 years or so.
It is important to agree on assumptions for future demand modelling that form the basis of these estimates, including:
- current and future transport network – usually includes funded infrastructure and services projects, but could also include committed projects, also consider transport network management and active transport modes. In some cases this can include all projects that are outlined in strategic planning documents, such as transport and land-use strategies, even if they have not been committed to and fully funded.
- rates of growth in travel demand – projected from estimates of population growth, demographic trends, economic development, employment levels, land use planning etc
- changes in road network management, such as vehicle priority, traffic management, high occupancy vehicle lanes, speed limits
- changes in pricing – tolls, parking, fuel costs, taxes, etc
- changes in mode share, due to changes in vehicle operating costs, public transport services, fares, interchanges, parking charges, the value of travel time, etc
- changing in parking supply, availability, pricing, including park and ride facilities
- data available to be used in a transport model, including population estimates, household travel surveys, elasticity of demand estimates, stated or revealed preference surveys etc
Further reading: ATAP.2016. T1 Transport Demand Modelling
Risk and Uncertainty
The latest Infrastructure Australia guide (2021) provides a useful distinction between risk and uncertainty – risk is defined as ‘events that have probabilities of occurrence that are predictable and outcomes that can be estimated with some confidence’, while uncertainty are ‘events where probabilities of occurrence are difficult to predict, and outcomes are challenging to quantify’.
