Dec 22, 2017
This report documents work to update the UK Department for Transport's national car ownership models (NATCOP) to reflect a 2011 base year, and to enhance the models to take account of DfT's experience in applying the previous version of the models which worked with a 2001 base year. The NATCOP models are estimated from observations of household car ownership choice collected between 1971 and 2014, and represent cross-sectional variation in car ownership as a function of household income, household type, area type, licence holding, number of workers, running and purchase costs, and company car ownership. The model formulation also explicitly represents the impact of saturation as a function of area and household type estimated from the 1971 to 2014 period over which the general pattern has been for car ownership to rise towards saturation.
The update to the car ownership model involved assembling more recent choice and other input data so that the models could be estimated to reflect a 2011 base year. A number of enhancements were also made to the model specification, in particular the separation of the previous London area type into Inner and Outer London to better represent variation in ownership across the capital, the addition of a continuous population density term to better represent lower ownership in the densest areas, and the development of a licence cohort model that projects growth in licence holding by age-gender cohort and that can be run for different future scenario assumptions.
Phase 1 of the project involved a review of the previous 2001 base version of the model, in particular by comparing forecast 2011 car ownership levels to observed 2011 Census data.
Validation of total car ownership predictions for 2011 demonstrated that the model performed well across Great Britain as a whole, and reasonably well for the four non-London area types. However, for London the model over-predicted ownership and further investigations demonstrated that the predictive performance was worst in Inner London.
In addition to the area type validation, the models were validated by examining how the predicted probabilities of the zero-, one-, two- and three-plus-car alternatives varied by population density. This validation demonstrated that while the over-prediction of one-car households persists across the whole range of observed population densities, the errors in multiple car ownership show a clear relationship with population density with car ownership over-predicted in the densest areas.
To investigate the performance of the model in London further, the relationship between car ownership and population density was explored for each of the individual London boroughs. This demonstrated that there was an 'Inner London' effect in addition to the population density effect, which reduced the likelihood of car ownership.
As a result of this analysis a key recommendation in the first phase of the study was to test separate area types for Inner and Outer London, as well as population density terms across all area types.
Following the Phase 1 recommendations, the London area type in the previous model was split into Inner and Outer London area types. Significantly lower saturation levels were estimated for the Inner London area type, however no significant differences in the income sensitivities were identified between the two area types.
In addition, new population density terms capture variation in car ownership behaviour over and above that represented by the variation in saturation and income sensitivity with area type. In all three models statistically significant terms have been identified that capture that the probability of owning cars decreases as population density increases.
Using the estimation data, it was possible to identify significant PT accessibility terms, reflecting lower car ownership levels for households with good public transport accessibility. However, it was decided not to implement these terms on the basis that the improvements in model fit were relatively modest and because it would be difficult and time-consuming to make forecasts of how PT accessibility might evolve in the future.
For parking, while the estimation data collects parking information at the destination, the household data does not record information on parking cost and/or residents' parking schemes. Furthermore, even if such information were to be available it would again be difficult and time-consuming to assemble future forecasts of parking costs. Therefore no (household) parking terms have been included in the final model specifications.
One of the key improvements to the new car ownership model is an enhanced treatment of licence holding that has been achieved by the development of a new licence cohort model. In addition to the Great Britain average licences per adult (LPA) time trend term used in previous versions of NATCOP, cross-sectional variation in licence holding by age band and gender cohort has been incorporated in the model specification.
In implementation, the cohort model provides a mechanism for the models to take account of future changes in licence holding such as higher licence-holding rates for older females. The licence cohort model has been setup as a simple spreadsheet model and allows different assumptions around future licence holding acquisition rates to be tested.
Car ownership data
Review of previous model performance and specification
New model development
Summary and recommendations
Saturation estimation methodology
Licence cohort model