Dynamic adaptive transport policies for handling deep uncertainty

Many long-term transport policy decisions are made by assuming that (1) the range of possible futures is known well enough to predict future changes to the transport system, (2) there is enough knowledge regarding the correct transport system model to estimate policy outcomes, and (3) there is enough knowledge regarding the importance stakeholders currently assign to the various outcomes or will assign in the future. However, for long-term transport policy decisions these assumptions can often not be made, since decision makers, analysts, and experts do not know or cannot agree on (1) how the future will develop, (2) the system models, and/or (3) the value system(s) to be used to rank alternative policies. This paper presents a 'dynamic adaptive' approach to policymaking for long-term transport policies that aims at overcoming the shortcomings of traditional approaches for handling deep uncertainty. It allows adaptations in time as knowledge is gathered. The approach is illustrated with dynamic adaptive policies for solving various long-term problems in the fields of road, rail, and air transport.