Nijmegen School of Management, Radboud University, Nijmegen, The Netherlands.
Delft University of Technology, Delft, The Netherlands.
Sci Rep. 2023 Mar 15;13(1):4316. doi: 10.1038/s41598-023-30818-2.
Visioning has been widely adopted in transport planning as a method to support explorations of possible future transport systems over a long time horizon. There are vast variations in how visioning is applied but given a clear association between visions and the long-time perspective, it is unclear how these processes handle uncertainty surrounding the resulting visions and their implementation. This study reflects on previous visioning processes by systematically reviewing the scientific publications on participatory visioning in passenger transport. The review identifies possible improvements contributing to a systematic approach that produces concrete visions and actions to deal with uncertainties surrounding the vision and its implementation. We address these improvements by proposing a robust and generative visioning framework, which combines the generative approach in Appreciative Inquiry (Ai) and methods to handle uncertainty in the Dynamic Adaptive Planning (DAP). The framework is illustrated in a case study of the Southwest area of the Dutch city of the Hague that involved over 50 participants in a survey and two workshops. The process produced a vision for the mobility system of the area, a set of measures to realize it (i.e. pathways), and concrete actions to ensure that the pathways are robust against different futures that can affect the implementation. The approach can help planners, policymakers, and researchers in designing a visioning process that helps participants to better appreciate the temporal dimension of the visioning process and improves their awareness regarding the need to safeguard policy interventions against possible impacts of (un)certain future events.
愿景已经被广泛应用于交通规划中,作为一种支持对未来交通系统进行长期探索的方法。虽然愿景的应用方式存在很大差异,但由于愿景与长期视角之间存在明确的关联,因此不清楚这些过程如何处理与产生的愿景及其实施相关的不确定性。本研究通过系统地回顾关于客运参与式愿景的科学出版物,反思了以前的愿景过程。该审查确定了可能的改进措施,有助于形成一种系统的方法,生成具体的愿景和行动,以应对愿景及其实施所面临的不确定性。我们通过提出一个稳健且具有生成能力的愿景框架来解决这些改进问题,该框架结合了欣赏式探询(Appreciative Inquiry,Ai)中的生成方法以及用于处理动态自适应规划(Dynamic Adaptive Planning,DAP)中不确定性的方法。该框架在荷兰海牙市西南部地区的案例研究中得到了说明,该案例涉及了超过 50 名参与者的调查和两个研讨会。该过程生成了该地区移动系统的愿景、实现愿景的一系列措施(即路径),以及确保路径在可能影响实施的不同未来情况下具有稳健性的具体行动。该方法可以帮助规划者、政策制定者和研究人员设计一个愿景过程,帮助参与者更好地理解愿景过程的时间维度,并提高他们对需要保护政策干预措施以防止未来事件(不确定的)可能影响的认识。