时空大数据下城市建筑空间规划寻优仿真
Simulation of Urban Building Space Planning Optimization under Spatiotemporal Big Data
陈思笛 1蔡钢伟2
作者信息
- 1. 西南交通大学希望学院,四川 成都 610400
- 2. 同济大学建筑与城市规划学院,上海 200092
- 折叠
摘要
城市建筑空间规划涉及多个冲突的优化目标,如建筑利用率、交通拥堵和绿地覆盖率等.由于目标之间存在相互制约和矛盾,在对其规划时,难以达到较好的收敛速度和精度.因此,提出时空大数据下城市建筑空间规划寻优算法研究.上述方法针对城市建筑空间规划所需费用、空间紧凑度、适宜性以及环境兼容性等要素分别构建目标函数,并将各要素结合获取城市建筑空间规划模型;在时空大数据支持下,利用多目标寻优算法对城市建筑空间规划模型以及相应要素目标函数展开寻优处理,以此获取较为理想的城市建筑空间规划结果.经实验验证,所提算法的收敛速度快、收敛精度高,且利用以上算法获取的城市建筑空间规划结果与理想结果偏差小.
Abstract
At present,urban building space planning involves multiple conflicting optimization objectives,such as building utilization rate,traffic congestion,and green coverage rate.However,due to mutual constraints and contradic-tions between these objectives,it is difficult to achieve better convergence speed and accuracy.Therefore,this paper researched an optimization algorithm for urban building space planning based on spatiotemporal big data.Firstly,this method constructed objective functions for various elements,such as urban building space planning cost,spatial com-pactness,suitability as well as environmental compatibility,and then integrated these elements to obtain an urban building space planning model.With the support of spatiotemporal big data,the method used a multi-objective opti-mization algorithm to optimize the model and the objective function of corresponding elements,thus obtaining more i-deal planning results.The following conclusions can be drawn from experimental results.The proposed algorithm has fast convergence and high accuracy of convergence.Meanwhile,the urban building space planning result obtained by the proposed algorithm has small deviations from the ideal result.
关键词
时空大数据/城市建筑/空间规划/寻优算法/规划模型Key words
Spatio-temporal big data/Urban architecture/Spatial planning/Optimization algorithm/Planning model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据