Updating the UK National Car Ownership Model

Background

The National Trip End Model (NTEM) is maintained by the UK Department for Transport and is widely used for transport planning purposes. NTEM provides forecasts of population, employment and households by car ownership, as well as trip ends and traffic growth factors. These forecasts are made available for free online via the TEMPRO software. An important component of NTEM is the national car ownership model.

Goals

As a part of an Atkins project to update NTEM, RAND Europe updated and enhanced the national car ownership models.

The objective of the update was to re-estimate the car ownership models using more recent data, and to make improvements to the model specifications based on experience in applying the model. In particular, the previous model was known to over-predict car ownership in dense urban areas, particularly London. Consideration was also given on how to reflect relevant recent trends into the modelling approach, such as the tendency for young adults to delay licence acquisition.

Methods

The models were estimated using a combination of National Travel Survey data and Family Expenditure Survey data (and the successor surveys). The car ownership models use a particular form of the discrete choice model that incorporates a saturation effect that is directly estimated from the data. Data dating back to 1971 is used for model estimation, ensuring that the saturation terms are estimated from data that covers a wide range of different ownership levels.

Findings

Car ownership data

Analysis of the proportions of households owning zero, one, two and three-plus cars over the 1970–2015 period showed that the fraction of households owning one car has remained constant at around 45 per cent. However, the proportion of households owning no cars has fallen from just under half to just under one-quarter, while the proportions of multi-car households have increased considerably.

Review of model performance and specification

• Total car ownership predictions for 2011 demonstrated that the model performed well across Great Britain as a whole, though for London the model over-predicted ownership, particularly in Inner London. Further investigation found this ‘Inner London’ effect probably reflected factors such as higher congestion, constraints on parking supply, the impact of the congestion charge and high levels of public transport (PT) accessibility.
• The model yielded a consistent under-prediction of zero-car households across all area types. This is important in the context of forecasting public transport demand, as members of zero-car households are much more likely to travel by public transport than members of car-owning households.
• Errors in multiple-car ownership showed a clear relationship with population density, with ownership over-predicted in the densest areas.
• When the models were applied from a 2001 base to predict car ownership in 2011, it was assumed that there would be no change in company car ownership over the decade. However, as a result of taxation changes, company cars fell from around 10 per cent of total cars to just over 8 per cent of total cars, contributing to the over-prediction of multiple-car ownership in 2011.

Model development

• The 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.
• For parking, while the NTS 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.

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