Econometrics of UK Car Travel

Econometrics of UK Car Travel


Jonathan Vickers, Department for Transport, James Mabbutt, Department for Transport, Kenneth Koo, Department for Transport


The UK Department for Transport have developed an econometric model assessing various socio-economic and demographic factors and how they impact on an individual’s travel behaviour, giving important insights to current trends and future modelling.


Car traffic growth has eased compared to long-term growth rates across a number of countries over the past 15 years. In 2015 the UK Department for Transport conducted an extensive evidence review looking into the underlying trends and explanatory factors for this. It concluded that although the literature puts forward a number of potential reasons for this, there has been little attempt to quantify these factors.

We have developed a three-stage econometric approach to quantify the impacts on the decisions of whether to obtain a full driving licence, whether to own a car given a licence is owned, and how far to drive given car ownership. The use of logistic regression has allowed us to estimate the impact of various factors on the binary outcomes of the likelihood to hold a full driving licence and whether they own a car. For the continuous nature of the miles driven regression we have taken a least squares approach resulting in an expected driving mileage estimate.

Using the Department’s leading National Travel Survey, it has been possible to examine a wealth of information about the impacts that various socio-economic and demographic factors have on travel demand. We have looked at the impacts of age, income level, work status and occupation type, gender, distance to public transport, household composition and area type.

Initial work has highlighted the significant variability of travel demand across the sample. We have noted that many of the traditional variables used in forecasting traffic demand still matter but we have also found quantified evidence of a change in the strength of relationships and some interesting new results suggesting additional variables are important.

Notable results include the degree to which living in highly urbanised areas impact behaviour and the relationship between the age profile and demand for driving. We also split the data into periods to look at changes over time. This provided interesting insights in the potential changing roles of income and occupation type on the propensity to drive.

The work to date has been peer reviewed by leading consultancy, Cambridge Econometrics, who have judged the model as fit-for-purpose at this stage. We are working on developments to extend the results and increase the model’s robustness, ensuring accurate and insightful results prior to the ETC final report submission deadline.

The results have implications for transport economics, modelling and policy. Given the similarities in other developed economies that have also seen a levelling off in traffic growth, other attendees of the ETC would likely benefit from understanding this analysis and the key messages to take home.


Association for European Transport