Urban Planning with Multidisciplinary Competing Objectives

具有多学科竞争目标的城市规划

• 批准号: 9817690
• 负责人: Richard Balling
• 金额: $ 43.96万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2003-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9817690&HistoricalAwards=false
• 关键词: Urban; Planning; Multidisciplinary; Competing; Objectives

***9817690 BallingThis Urban Research Initiative (URI) project develops a new approach to urban planning for the future. The urban planning endeavor is complicated by the existence of multiple competing objectives that require trade-offs based on the relative importance of objectives. However, it is difficult for planners to numerically quantify the relative weight that the public places on each of the objectives. In this research, a new approach will be taken to search for a set of plans which are pareto-optimal. The beauty of the pareto set is that it is independent of the relative importances of the objectives. A non-pareto plan will be noncompetitive regardless of the relative importances of the objectives since a pareto plan exists which is better in every objective. This project will develop a genetic algorithm to search over millions of plans to obtain a pareto set. With the computing and modeling power that exists today, urban planners can consider many plans and this approach eliminates the vast number of non-pareto plans from consideration by decision-makers. Many of the research ideas were initially developed in a previous NSF-supported project in which a genetic algorithm was used to obtain pareto-optimal urban plans for a single high-growth city in Utah. There are five goals for this current project: 1) obtain pareto plans for a multi-city metropolitan region. Application to a region (the Wasatch Front metropolitan region) rather than a single city is a significant conceptual leap because regional planning must allow the individual cities to exercise a certain degree of planning autonomy. A two-level approach will be investigated in which macro-planning occurs at the regional level while micro-planning occurs at the city level. 2) expand plans to include additional regional objectives from the human environment. In addition to minimizing traffic congestion, cost, and change, metrics will be developed for the maximization of affordable housing and the maximization of economic development. 3) include regional objectives from the natural environment. In particular, metrics will be developed for the control of air pollution and the preservation of natural and open spaces in the planning process. 4) develop a graphical way in which information from the pareto plans may be assimilated by decision-makers. The project will use slider-bars and real-time graphics to convey the planning trends in the pareto set that are implied by the various competing objectives. 5) develop procedures based on formal decision theory to assist decision-makers in narrowing the pareto set to a final plan or plans. ***

***9817690 balling这个城市研究倡议（URI）项目为未来的城市规划开发了一种新的方法。城市规划工作由于存在多个相互竞争的目标而变得复杂，这些目标需要根据目标的相对重要性进行权衡。然而，对于规划者来说，很难用数字量化公共场所在每个目标中的相对权重。在本研究中，将采用一种新的方法来寻找一组帕累托最优的计划。帕累托集的美妙之处在于它与目标的相对重要性无关。无论目标的相对重要性如何，非帕累托计划都是非竞争性的，因为存在一个在每个目标中都更好的帕累托计划。该项目将开发一种遗传算法来搜索数百万个计划以获得帕累托集。有了今天的计算和建模能力，城市规划者可以考虑许多计划，这种方法消除了决策者考虑的大量非帕累托计划。许多研究想法最初是在以前的一个nsf支持的项目中发展起来的，该项目使用遗传算法来获得犹他州一个高增长城市的帕累托最优城市规划。目前这个项目有五个目标：1)获得一个多城市大都市地区的帕累托计划。将其应用于一个地区（Wasatch Front都市圈）而不是单个城市是一个重大的概念飞跃，因为区域规划必须允许单个城市行使一定程度的规划自主权。将研究一种两级办法，其中宏观规划在区域一级进行，而微观规划在城市一级进行。2)扩大计划，包括人类环境中的其他区域目标。除了最大限度地减少交通拥堵、成本和变化之外，还将制定指标，以实现经济适用房的最大化和经济发展的最大化。3)包括来自自然环境的区域目标。特别是，将在规划过程中制定控制空气污染和保护自然和开放空间的指标。4)开发一种图形化的方法，使决策者可以吸收来自帕累托计划的信息。该项目将使用滑动条和实时图形来传达帕累托集合中各种竞争目标所隐含的规划趋势。5)开发基于正式决策理论的程序，以帮助决策者将帕累托集合缩小为一个或多个最终计划。＊＊＊