Forecasting demand and the economic basis of transport forecasts.
You need to be able to:
Explain why the government forecasts demand for transport
Analyse how the government forecasts demand for transport
Discuss the problems of forecasting future demand for transport
Why the Government Forecasts Demand for Transport Forecasts are used to determine the future network requirements (the so called ‘predict and provide’ approach) which fulfils a role in transport policy in that it estimates where the greatest congestion bottlenecks will be in the future.
Allows comparison of potential infrastructure projects to see which ones will create the greatest benefits for society. If the government predicts where the worst bottlenecks will be, they can invest their limited resources in the projects that create the largest net benefits to society.
Allows them to formulate effective future transport policies. For example, creating integrated policies like the Oyster Card system in London.
How the Government Forecasts Demand for Transport
Higher the demand for goods & services and commuter transport
Goods and services require freight transport / commuting requires passenger transport
Affordability of transport increases
Some forms of transport are income elastic (YED)
Higher prices of fuel
Cars less affordable to run
XED as people switch from cars to alternatives.
Greater demand for goods and services
Derived demand for freight transport
Increase in supply of cars
Fall in price of cars (link to diagram)
Greater accessibility to cars increases number of car journeys
Increase in net exports
More goods and services are imported and exported
Increase in derived demand for freight transport
Fall in price of public transport
Substitute to car use
XED for public transport leads to modal switch
Changes in future tax rates
Changes in government policy
The Difficulties of Forecasting Future Demand
Estimates: the forecasts are only ever estimates (L2) OR are often based upon estimated data (eg future GDP) (L2). These forecasts are unreliable and may be wrong (basic level 3) – hence any policy based entirely on such forecasts could therefore be invalid (good L3).
Uncertainty: the volatility of some of these variables also adds to their inaccuracy (L2). For example, oil and fuel prices have been highly volatile over the last few years and, therefore, any forecasts based upon this data may only be accurate in the very short term (L3).
Extrapolation: using past data/trends can be a problem as the more we extrapolate in to the future the greater the possibility of errors creeping in. (L3)
Assumptions of causality: forecasts make assumptions as to patterns of causality (L2) and these may be wrong – hence the forecasts will also be wrong too (L3).
Surveys: these may