SCALE (SMALL CHANGES LEAD TO LARGE EFFECTS): Changing Energy Costs in Transport and Location Policy

交通和居住地点消耗大量的能源，同时只服务于促进主要的经济和社会活动。城市形态与旅行模式之间的关系本质上是复杂的：它可以受到政策的影响，但通过许多个人的反应，而不是受到明确的控制。因此，管理运输中使用的能源是一个间接的过程，它通过影响旅行的数量和距离、旅行的方式以及由此产生的旅行的能源需求来起作用。要有效地实现这一目标，就需要充分了解产生旅行需求并影响满足其供应方式的许多复杂的相互作用的社会过程。复杂性科学为组织这种理解提供了一个框架。在本项目中，我们认为能源成本的变化会在城市等复杂系统中产生令人惊讶和意想不到的影响，这主要是因为当运动的变化和用于维持地点的能源公用事业产生难以追踪甚至更难控制的乘数效应时产生的许多顺序效应。例如，随着能源成本的增加，人们最终会达到一个阈值，超过这个阈值，他们就无法维持现有的旅行模式，甚至无法维持他们的位置，然后他们的行为就会发生快速变化。当能源成本降低时，这些变化绝不是对称的，因为人们通过改变地点和模式从一种活动切换到另一种活动。在伦敦大学学院，我们有四组研究人员建立了城市和交通系统的模型，为这些对能源成本变化的反应提供了相关的视角。威尔逊开创了熵最大化运输和位置方法的发展，其中能源和旅行成本是旅行的基本决定因素，他最近的工作是将这些模型嵌套在产生意想不到的影响的动态中，这是理解能源成本变化所影响的各种变化的关键。以一种互补的方式，这些模型可以使用最近的空间集聚理论提供更强有力的理论基础，这些理论可以追溯到图灵，但在克鲁格曼（TK模型）的工作中找到了最清晰的表达。这些模型为威尔逊发展的玻尔兹曼-洛特卡-沃尔泰拉（BLV）模型提供了信息。将这些模型转化为物理基础设施涉及网络科学的明确发展，Zhou和Heydecker的模型提出了通过将物理网络与BLV和TK模型产生的流量联系起来来降低能源成本的方法。我们在这里提出的是扩展和发展这三种方法，扩展我们现有的大伦敦地区的土地使用运输模式（作为廷德尔中心城市项目的一部分），使我们的合作伙伴能够探索涉及城市能源成本变化的“如果”问题。我们将采用的方法来探索这些模型涉及非线性，这些非线性是由n阶乘数效应特有的复杂系统中的正反馈效应引起的。我们将使用相空间表示来可视化这些变化，然后在可操作的土地使用运输模型中实施这些变化，我们将向合作伙伴传播这些变化，以提出重大的政策问题。我们打算提供一系列紧密耦合的可交付成果，将这门科学推进到政策制定者和专业人士可以直接使用的程度。我们将使用相关项目下正在开发的各种基于网络的服务来交流我们的发现。通过这种方式，我们将在最佳科学的基础上发展最佳实践。我们相信，我们可以向更广泛的选民展示复杂性科学的基本逻辑，从而深入了解这些最热门的能源成本变化问题。

Transport and residential location consume substantial quantities of energy whilst serving only to facilitate primary economic and societal activities. The relationship between urban form and travel patterns is inherently complex: it can be influenced by policy but through many individual personal responses rather than being subject to explicit control. Managing the energy used in transport is therefore an indirect process that works by influencing the amount and distance of travel, the means by which travel takes place, and the energy requirement of the resulting travel. Achieving this effectively requires an a full understanding of the many complex interacting social processes that generate the demand for travel and impinge on the ways in which it is satisfied in terms of its supply. The complexity sciences provide a framework for organising this understanding. In this project, we argue that changes in energy costs generate surprising and unanticipated effects in complex systems such as cities, largely because of the many order effects that are generated when changes in movement and the energy utilities used to sustain locations generate multiplier effects that are hard to trace and even harder to contain. For example, as energy costs increase, people eventually reach a threshold beyond which they cannot sustain their existing travel patterns or even their locations and then rapid shifts occur in their behaviour. When energy costs reduce, these shifts are by no means symmetrical as people switch out of one activity into another, by changing location as well as mode.At UCL, we have four groups of researchers building models of urban and transport systems which provide related perspectives on these responses to changing energy costs. Wilson pioneered the development of entropy maximising approaches to transport and location in which energy and travel costs are essential determinants of travel and his recent work in nesting these models within a dynamics that generate unanticipated effects is key to understanding the kinds of changes that are now being effected by changing energy costs. In a complementary way, these models can be provided with a much stronger rationale using recent theories of spatial agglomeration which date back to Turing but find their clearest expression in the work of Krugman (TK models). These models thus inform the Boltzman-Lotka-Volterra (BLV) models developed by Wilson. Translating these models into physical infrastructures involves explicit developments in network science and Zhou and Heydecker's models suggest ways in which energy costs might be reduced by linking physical networks to flows generated by the BLV and TK models. What we propose here is to extend and develop these three approaches, extending our existing operational land use transport model for Greater London (built as part of the Tyndall Centre's Cities programme) to enable our partners to explore 'what if ' questions involving changing energy costs on the city.The methodologies we will employ to explore these models involve nonlinearities that are caused by positive feedback effects in complex systems where n'th order multiplier effects are endemic. We will use phase space representations to visualise such changes and then implement these in the operational land use transport model which we will disseminate to our partners in the quest to pose significant policy questions. We intend to provide a series of tightly coupled deliverables to progress this science to the point where it is directly usable by policy makers and professionals. We will communicate our findings using various kinds of web-based services being developed under related projects. In this way, we will develop best practice based on best science. We believe that we can demonstrate the essential logic of complexity science to a much wider constituency in developing insights into these most topical questions of the changing cost of energy.