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基于居民步行活动模拟的社区生活圈规划:研究框架与议题
车冠琼1, 仇保兴2, 王倚天3, 何继新4
1.天津城建大学建筑学院,讲师;2.( 通讯作者):中华人民共和国住房和城 乡建设部,教授,qiubx@mohurd.gov.cn;3.天津大学建筑设计规划研究总院有限公 司,工程师;4.天津城建大学经济与管理学院,教授
摘要:
社区生活圈是城市居民生活的基本单 元,从居民日常行为活动出发进行生活圈规划设 计,是以人为本的新型城镇化背景下提升居民生 活品质的重要途径。研究以主体建模、空间设计 相关理论为基础,在系统回顾国内外生活圈及 居民步行活动模拟研究进展的基础上,从数据 获取、模型构建、模拟分析、结果应用四方面, 阐释基于居民步行活动模拟的社区生活圈研究 框架。提出基于主体建模的生活圈步行活动模 拟模型构建、多场景居民步行活动模式挖掘、居 民步行活动影响机制、生活圈空间设施精准规 划与动态规划四大关键议题,为实现以人为本 的生活圈规划提供路径参考。
关键词:  社区生活圈  多主体模型  步行活动模 拟  空间精准规划  研究框架
DOI:10.13791/j.cnki.hsfwest.20240220
分类号:
基金项目:国家社会科学基金一般项目(22BSH125)
Community planning based on the walking activity simulation: Research framework andtopics
CHE Guanqiong,QIU Baoxing,WANG Yitian,HE Jixin
Abstract:
Community living area is the basic unit of residents’ life. It is delineated on the basis of walking distance, and the core meaning of community living area is to provide residents with convenient and comfortable living environment within walking distance. Therefore, planning and design of community living area from residents’ daily walking activities is an important way to improve residents’ living quality in the context of people-oriented new urbanization. Based on the theories of agent-based model and spatial design, this paper systematically reviews the research progress of community living area and residents’ walking activity simulation at home and abroad. Generally, research in community living area has begun to shift from object-oriented to peopleoriented, among which, some studies focused on residents’ walking activities and their interaction with the spatial environment of the living area, using methods such as correlation analysis and regression analysis. However, the above research still needs to be further deepened in the following two aspects. Firstly, the excavation of residents’ walking activity patterns needs to be deepened. At present, residents’ walking activities are mainly obtained through GPS, activity logs, which have limited samples and high costs, making it difficult to comprehensively summarize residents’ walking activity patterns. Secondly, analysis of the walking activity mechanism in the living area needs to be deepened. Previous studies mainly focused on a certain type of space in the living area. As a result, the examination of the spatial elements of the living area was not comprehensive enough. In addition, previous studies mostly used linear regression to analyze the impact of the spatial environment on walking activities, which can draw the significance of the impact of spatial environmental elements on residents’ walking activities, but fails to draw the impact range of the spatial environmental elements, which makes it difficult to provide precise guidance for spatial optimization. Therefore, this paper develops a research framework for community living area planning based on residents’ walking activity simulation. Four research steps are included. First is data acquisition, using a combination of survey and data mining to obtain data on residents’ walking activities and the spatial environment of the living area. Second is model construction. The agent-based walking activity simulation model is established using software such as Netlogo, AnyLogic, etc. The walking activity and spatial environment characteristics are translated into the model parameters with mathematical and spatial analysis. Third is simulation analysis, which focuses on walking activity pattern mining and the influence mechanism of communities’ spatial environment on walking activity. Finally, it is the precise optimization strategy and dynamic planning for the spatial design of the living area. On this basis, this paper presents four key research subjects. First is the construction of the agent-based walking activity simulation model. The key to this part is the establishment of simulation rules for residents’ walking activities. In terms of destination selection rules, the discrete choice model can be adopted, in which residents choose their destinations on the basis of evaluating the spatial environment of their living area and on the basis of the principle of maximizing utility. In terms of path selection rules, Space Syntax theory and method can be applied to calculate paths of shortest metric distance, minimum angular distance, and minimum topological distance. Second is residents’ walking activity patterns mining under multi-scenario simulations. Through the adjustment of model variables and parameters, this model can reflect the individual differencesof residents in terms of age, gender, family structure, etc., and the differences of spatial environment in different living areas in terms of building density, functional mixing, street density, etc. Then, with assignment of corresponding residents to corresponding space, multi-scenario simulations such as shopping, commuting, and recreations can be realized. On this basis, by means of cluster analysis, patterns of walking activities can be identified more comprehensively. Third is the influence mechanism of spatial environment on residents’ walking activity. With combination of the simulation and machine learning, the Gradient Boosting Decision Tree method can be used to identify the spatial variables that have a significant impact on walking activities through the contribution of independent variables, and the nonlinear relationship is portrayed by Partial Dependency Plot, so as to find out the threshold and range of spatial environment elements adjustment. Four is the precise and dynamic planning of space and facilities in the living area. According to the threshold range, a more precise optimization strategy can be proposed for the spatial environment such as walking environment, open space and service facilities. In addition, the model can be used to simulate and predict the spatial and temporal distribution of residents’ walking activities under different spatial planning schemes and with the change of residents’ demand, which can provide prediction and advice for spatial design adjustments.
Key words:  community  multi-agent-based model  walking activity simulation  precise spatial planning  research framework