The effectiveness of soft transport policy measures: A critical assessment and meta-analysis of empirical evidence

Abstract

In the last few years there has been a growing interest in transport policy concerning behaviour oriented ‘soft’ measures to reduce private car use. Besides an assessment of the methodological quality of available evaluation results, the present paper focuses on a quantitative, meta-analytical synthesis of this empirical evidence. For these purposes a data set of 141 studies evaluating three types of soft transport policy measures was compiled mainly from already published narrative research reviews. The ability to draw strong causal inferences from the available research evidence is limited by the fact that all the retrieved evaluation studies use weak quasi-experimental designs. At least for one policy measure type our analyses also indicate the presence of a reporting bias. Across all three soft policy measures we found a statistically significant random-effects pooled effect size of 0.15. Translated into the original metric such an effect size indicates an increase in the no-car use proportion from 39% to 46%.

Introduction

In the last few decades car use has increased considerably all over the world. The rise in car use is associated with the problems of congestion and pollution that we are all so familiar with. Some of the pollution-related problems can be tackled by reduction in fuel consumption and ‘cleaner’ vehicle technology. However, other car use related problems cannot be solved by improvements in motoring technology. These include the threats to individual health (through road traffic casualties), the economy (through congestion and time lost), the environment (in terms of land use, noise and effects on wildlife etc) and our communities (severance and loss of community space). These problems can only be solved, if the total level of car use is reduced or at least if further increase is stopped (e.g. Steg & Tertoolen, 1999; Vlek & Steg, 1996).

Whereas the problem diagnosis is clear, finding effective ways for reducing car use seems to be difficult. For this purpose most local authorities have tried out ‘hard’ policy measures in the last decade such as physical improvements to transport infrastructure or operations, traffic engineering, control of road space and changes in price. However, despite huge financial investments, these ‘hard’ infra-structural initiatives alone failed to deliver the shifts from car use that were hoped for and expected (e.g. Stopher, 2004). Similar experiences have been made with transport pricing strategies: Because of the high political costs associated with pricing, politicians were and still are very reluctant to adopt them (e.g. Schade & Schlag, 2003).

These sobering experiences are probably the background for transport policy's growing interest in a range of initiatives which are widely described as ‘soft’ transport policy measures. Typical examples of soft transport policy measures are workplace travel plans, personalised travel planning, public transport marketing, and travel awareness campaigns. A consistent definition has not yet been developed to identify what constitutes a ‘soft’ measure. The word ‘soft’ is sometimes used to distinguish these initiatives from the above mentioned ‘hard’ measures, although soft measures often include such, ‘hard’ elements. For example objective improvements of service quality are an important prerequisite for effective public transport marketing; parking fees and restrictions are main elements of effective workplace travel plans. ‘Soft’ also refers to another typical feature of these measures: they try to influence individual decision making less by using force and restrictions, but rather by persuasion that is by changing people's perceptions and motivations. For this purpose soft transport policy measures systematically use social marketing technologies (e.g. Ampt, 2003) in which psychological concepts like perceptions, values, attitudes, social norms or perceived self-efficacy play an important role.

Thus, for applied environmental psychologists the growing interest in soft transport policy measures opens the chance of directly contributing to the development of new problem solving strategies in the central environmental policy field mobility. However, political decision makers are less interested in the details of our academic theoretical and methodological debates but more in the general picture that emerges from momentarily available empirical evidence concerning the capacity of soft transportation measures to reduce people's car use. In other terms decision makers need good research synthesis.

Recent change in the transportation policy strategy of the British government provides an example of how research synthesis can influence the decision to implement new problem solutions: In its search for effective car use reduction measures in the last few years, the British Department of Transportation commissioned a series of scientific reports (Atkins, 1999; Cairns, Sloman, Newson, Anable, Kirkbride, & Goodwin, 2004; Dodgson, Pacey, & Begg, 2000; Dodgson, Sandbach, McKinnon, Shurmer, van Dijk, & Lane, 1997; Halcrow Group, 2002; James, 2002; Sloman, 2003; Steer Davies Gleave, 2003a) reviewing national and international evidence in order to make estimates of the overall effect of a combination of soft transport policy measures on traffic levels in British conditions. In the last of these reviews Cairns et al. (2004) develop a ‘low intensity’ and ‘high intensity’ impact scenario of future soft transport policy measures implementations. In the low intensity scenario they assume that local authorities would carry on introducing these initiatives, so there would be a gradual growth in the number of schemes and not a step change. In the high intensity scenario Cairns et al. (2004) assume that there would be much more activity and many more resources than at present. For their low intensity scenario, Cairns et al. (2004) estimate that UK wide traffic could be cut by about 3% and peak hour urban traffic by about 5%. In their high intensity scenario they estimate that traffic cuts up to 11% and peak hour urban traffic cuts up to 21% should be possible nationally.

Explicitly referring to the optimistic results of the Cairns et al. (2004) review, the British government has decided to integrate soft transport policy measures as a vital part of their local transport strategy. As a consequence of this decision, the British government will invest substantial financial resources in motivating authorities to implement soft travel measure programs at the regional and local level (UK Department of Transportation, 2005) in the next 10 years.