A Multi-Objective Optimization of Physical Activity Spaces

摘要

优化体育活动空间（PAS）的选址以确保健康、公平和效率一直是城市规划中的一个重要议题。鉴于城市绿地（UGS）的健康效益，本研究以杭州市拱墅区为例，探讨体育活动空间应如何选址以优化三个目标：（1）最小化体育活动空间与城市绿地之间的距离；（2）最大化体育活动空间的可达性；（3）最大化覆盖范围内的人口数量。本研究开发了一个体育活动空间多目标优化模型（MOPAS），基于多目标粒子群优化算法，得到一组非支配的帕累托最优解，用于确定冲突目标之间最实际的权衡。文章比较了帕累托解的优缺点，并评估了选址的建设情况和实施可行性。决策者可以根据主观偏好和客观条件选择最佳方案。MOPSO有望显著改善体育活动空间选址优化问题，其应用方法也可以推广到全球城市规划设施的多目标优化中。

Abstract

Optimizing the location of physical activity spaces (PAS) to ensure health, equity and efficiency has long been an important issue in urban planning. Given the health benefits of urban green spaces (UGS), taking Gongshu District in Hangzhou as a case, we examine the issue of where such PAS should be located to optimize three objectives: (1) minimize the distance between PAS and UGS; (2) maximize the accessibility of PAS and (3) maximize the population that falls within the coverage range. This study develops a multi-objective optimization of physical activity spaces model (MOPAS) based upon multi-objective particle swarm optimization to yield a set of non-dominated Pareto optimum solutions that can be used to determine the most practical tradeoffs between the conflicting objectives. It compares the advantages and disadvantages of the Pareto solutions and evaluates the construction situation of locations and the implementation feasibility. Decision-makers can choose the best solution according to subjective preferences and objective conditions. The MOPSO holds great promise for improving the location optimization of PAS and the methods applied can be adapted to support multi-objective optimization of facilities in urban planning globally.

引用格式

@article{2022_land_physical_activity,
  title={A Multi-Objective Optimization of Physical Activity Spaces},
  author={蔚芳 and Wenwen Xu and Chen Hua},
  journal={Land},
  year={2022},
  doi={10.3390/land11111991}
}