Smarter Choices: Assessing the Potential to Achieve Traffic Reduction Using ‘Soft Measures’

Abstract

In recent years, there has been a growing interest in a range of transport policy initiatives which are designed to influence people’s travel behaviour away from single‐occupancy car use and towards more benign and efficient options, through a combination of marketing, information, incentives and tailored new services. In transport policy discussions, these are now widely described as ‘soft’ factor interventions or ‘smarter choice’ measures or ‘mobility management’ tools. In 2004, the UK Department for Transport commissioned a major study to examine whether large‐scale programmes of these measures could potentially deliver substantial cuts in car use. The purpose of this article is to clarify the approach taken in the study, the types of evidence reviewed and the overall conclusions reached. In summary, the results suggested that, within approximately ten years, smarter choice measures have the potential to reduce national traffic levels by about 11%, with reductions of up to 21% of peak period urban traffic. Moreover, they represent relatively good value for money, with schemes potentially generating benefit:cost ratios which are in excess of 10:1. The central conclusion of the study was that such measures could play a very significant role in addressing traffic, given the right support and policy context.

Acknowledgements

The support of the Rees Jeffreys Road Fund, which has enabled the preparation of this article, is very gratefully acknowledged. In addition, the authors wish to gratefully thank the client team at the Department for Transport, all the different experts and practitioners working in this area who contributed to the study and the reviewers of this article for their helpful comments.

Notes

1. The term ‘soft’ has been used in the literature for several years, albeit with some misgivings. The term ‘smarter choices’ was coined at the final stage of this study by the Department for Transport clients, as an alternative label for the types of measures studied.

2. As previously mentioned, the change in the proportion of staff commute trips made as a car driver was used as the main measure of workplace travel plan impact.

3. In both models, the reduction in car driver kilometres was assumed to be directly proportional to the reduction in car driver trips, since there was no available evidence on which to base an alternative calculation or which suggested otherwise.

4. Since the interview work for our study took place, the Department for Transport and Department for Education and Skills issued an explicit aim that all schools should have travel plans in place by the end of the decade.

5. The estimates of costs per vehicle mile reduced are now being used, by others, to calculate the follow‐on costs of marginal carbon abatement. However, note that to find the full resource cost of saving carbon by these measures, it is necessary to allow for the net economic costs and benefits of the traffic reductions. In general, the effect of allowing for the wider costs and benefits will tend to be cost‐reducing, meaning that the overall net welfare cost of reducing a tonne of carbon by these measures will be reduced, and potentially outweighed by the benefits. This means that the marginal carbon abatement costs for these measures may be more favourable than appears at face value.

6. This data source also includes values for accidents, noise, pollution, climate change, infrastructure costs, a quantitative estimate for other unquantified factors and adjustments for taxation. However, the non‐congestion elements are based on less well‐established evidence, and were therefore not used in our report.