Destination Choice: The Underestimated Dimension in Sustainable Travel Behaviour

There have been two main tools used for assessing the role of behavioral change in transport to help to achieve large scale carbon reductions, such as the 80% reduction by 2050 discussed in the United Kingdom (UK), United States of America (USA) and other countries. These tools are (a) extrapolation from evidence on demand elasticities and case studies on public transport, 'smart' measures and demand management; and (b) a new generation of multi-modal 'strategic' transport models, often operating at national level and containing many of the best features of local network based models. Both approaches have been dominated by consideration of mode switching. -The discussion of 'behavioral change' and 'mode switching' are treated as meaning the same thing. For example, judgements that only very short car trips can be transferred to walk or cycle, and only car trips on main public transport corridors can be transferred to public transport. The result of this thinking has been an unintended underestimation of the scope for behavioral change. A whole dimension of choice is omitted, namely destination choice. Land-use/transport interactions are rarely considered,so land use changes are taken as exogenous rather than responding to transport conditions. In addition the absence of a specific network and specific geography in strategic models means that destination choice becomes invisible even within a fixed land-use pattern. The result is to treat the trip pattern, or distribution of trip distances, as fixed or exogenous. In models which do allow both mode and destination choices to vary, it is often found that destination choice is the more variable, and indeed is sometimes the necessary pre-condition to mode switching: a car trip to an out-of-town shopping center can change to a walking trip if the destination is shifted to local, or to public transport if the destination is shifted to a town center. The paper reviews the evidence on this topic, including local studies and aggregate trends in trips and average distances. The paper argues that the tools and assumptions used are unnecessarily making a challenging task seem even more difficult than it needs to be.