Improving scenario methods in infrastructure planning: A case study of long distance travel and mobility in the UK under extreme weather uncertainty and a changing climate
Improving scenario methods in infrastructure planning: A case study of long distance travel and mobility in the UK under extreme weather uncertainty and a changing climate
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DOI：
10.1016/j.techfore.2016.10.002
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发表时间：
2017-02
影响因子：
12
通讯作者：
A. Zanni;M. Goulden;T. Ryley;R. Dingwall
中科院分区：
文献类型：
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作者：
A. Zanni;M. Goulden;T. Ryley;R. Dingwall
本文通过英国 (UK) 的案例研究开发了一种基础设施规划的混合方法，该研究考察了气候变化对伦敦和格拉斯哥之间的长途旅行和流动性的影响。采用了定性方法（系统定性预见 (SQF)）和使用离散选择陈述偏好方法的定量模拟的新颖组合。主要数据集是对伦敦和格拉斯哥 2000 多名居民的出行行为调查。结合社会、技术和气候维度开发了三个基于 SQF 的说明性情景。对于每个场景，通过聚类分析生成的两组受访者对长途旅行模式的选择是使用陈述偏好调查数据进行模拟的，以描述每个场景中参与者可能做出的选择。我们证明了在创建基础设施规划决策工具时考虑各种变量的重要性。我们的研究结果表明，与天气相关的干扰会对旅行行为产生影响，尽管旅行很重要，但仍有相当多的旅行者决定不旅行。然而，绝大多数旅行者仍然会出行。政策制定者和交通基础设施负责人应该考虑这一点，以提高其对极端天气和需求的抵御能力，并更好地制定应急计划来控制和尽量减少中断对用户的影响。所描述的方法对基础设施规划具有更广泛的影响，特别是它能够吸引更广泛的利益相关者并避免线性预测模型。通过强调创建合理的决策空间，它提供了提高基础设施规划的稳健性和弹性的可能性。
This paper develops a mixed method approach to infrastructure planning through a United Kingdom (UK) case study examining the impact of a changing climate on long distance travel and mobility between London and Glasgow. A novel combination of a qualitative method - Systematic Qualitative Foresight (SQF) - and quantitative simulation using discrete choice stated preference methods is applied. The main dataset is a travel behaviour survey of over 2000 residents of London and Glasgow. Three illustrative SQF-based scenarios are developed incorporating society, technology and climate dimensions. For each scenario, the choice of long-distance travel mode by two groups of respondents generated by cluster analysis is simulated using stated preference survey data to describe the choices likely to be made by actors within each scenario.We demonstrate the importance of considering a wide range of variables when creating instruments for infrastructure planning decisions. Our results show that weather-related disruption has consequences for travel behaviour, with a considerable number of travellers deciding not to travel despite the importance of their trip. However, the vast majority of travellers would still travel. This should be considered by policy makers, and those responsible for transport infrastructure, in order to increase its resilience to extreme weather and demand, and better devise contingency plans to contain, and minimise, the effect of the disruptions on the users. The method described has wider implications for infrastructure planning, particularly in its ability to engage a broader range of stakeholders and to avoid linear models of prediction. By emphasising the creation of a plausible decision space, it offers the possibility of increased robustness and resilience in infrastructure planning.