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

By Sally Cairns, L. Sloman, C. Newson, Jillian Anable, A. Kirkbride, P. Goodwin. 2008

265 citations recorded by the source corpus.

Subfields: Renewable Energy, Sustainability and the Environment, Transportation

View Paper: https://www.semanticscholar.org/paper/8e39cd9e94442f1339f1ea5319358eefcad4f9f8

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In Renewable Energy, Sustainability and the Environment

In Transportation

Citations in the corpus

Long-term Transport Energy Demand and Climate Policy: Alternative Visions on Transport Decarbonization in Energy Economy Models (2014, methodology): “According to a review by Cairns et al, “soft mobility management” measures including teleworking, school travel plans and transport awareness campaigns can reduce overall traffic levels by about 11% within ten years [56].”
Evolving Narratives of Low-Carbon Futures in Transportation (2016, background): “Similarly, a UK study identified a potential of altogether 11% in reduced traffic by soft measures such as marketing, information and tailored new services (Cairns et al., 2008).”
Soft Transport Policy Measures: Gaps in Knowledge (2011, background): “For instance, any measure which reduces congestion has the potential to cause induced traffic, thus eroding its benefits (Cairns et al. 2008).”
Behaviour Theory and Soft Transport Policy Measures (2011, background): “Several narrative reviews (Brög et al., 2009; Cairns et al., 2008; Richter et al., 2010a; Taylor, 2007) have concluded that soft”
Modelling transport energy demand: A socio-technical approach (2012, background): “…evidence base, or even just a renewed appreciation of existing evidence, of the potential for behaviour to alter in ways which mean that reductions in the demand for travel activity and associated energy are both plausible and cost effective (Sloman et al., 2010; Cairns et al., 2008; Goodwin, 2008.”
Part II: Policy instruments for sustainable road transport (2010, background): “Thus, studies find that soft polices may reduce traffic levels by between 4 and 26 per cent (Cairns et al., 2008, p. 597).”
Beyond Technology: Demand-Side Solutions for Climate Change Mitigation (2016, background): “Altogether, behavioral measures, including marketing, information provision, and customized new services, such as car-sharing services, may reduce transport demand by approximately 10% (41, 58).”
Analysing cycling as a social practice: An empirical grounding for behaviour change (2015, background): “However, with cycling levels remaining low in the UK (DfT, 2014), for the past ten years ‘soft’ policy measures have attracted increased attention (Bamberg et al., 2011; Cairns et al., 2008).”
What cognitive mechanisms predict travel mode choice? A systematic review with meta-analysis (2017, background): “and quickly implemented compared to, for instance, engineering solutions (Cairns et al., 2008; Richter, Friman, & Gärling, 2010).”
Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK (2013, background): “Further work could also look into the recycling of any large amounts of tax revenues to other low carbon policies such as those aimed at reducing the need to travel, or travel by more sustainable modes (Cairns et al., 2008).”
Understanding the Underutilization of Urban Bicycle Commuting: A Behavioral Reasoning Perspective (2014, background): “Indeed, several meta-reviews of the respective transportation literature seem to suggest that policies and social marketing programs are critical in achieving changes in individuals’ choice of travel mode (Brög et al. 2009; Cairns et al. 2008; Möser and Bamberg 2008).”