系统仿真学报 ›› 2019, Vol. 31 ›› Issue (7): 1321-1333.doi: 10.16182/j.issn1004731x.joss.18-0833

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城市安全宜居度变化多智能体仿真模型

潘理虎1,2, 李晓文1, 秦世鹏1, 张英俊1, 闫慧敏2   

  1. 1. 太原科技大学 计算机科学与技术学院,山西 太原 030024;
    2. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2018-12-14 修回日期:2019-03-01 发布日期:2019-12-12
  • 作者简介:潘理虎(1974-),男,河南上蔡,博士生,副教授,研究方向为人工智能、软件工程。
  • 基金资助:
    深圳市战略新兴产业发展专项资金(JSGG20170413173425899), 山西省-中科院科技合作重大项目(20151101001)

Multi-Agent Simulation Model of Urban Safety Livability Change

Pan Lihu1,2, Li Xiaowen1, Qin Shipeng1, Zhang Yingjun1, Yan Huimin2   

  1. 1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
    2. Institute of Geographic Science and Natural Resource Research, Chinese Academy of Science, Beijing 100101, China
  • Received:2018-12-14 Revised:2019-03-01 Published:2019-12-12

摘要: 城市宜居度是影响现代城市建设决策的重要因素,据此提出运用多智能体建模理论创建城市宜居度变化模型,以分析复杂城市系统中城市宜居度状况及其变化趋势。以安全性为例,给出了治安、消防、交通、应急和居住五项环境指标和政策影响下居民的决策行为与城市安全宜居度间的交互动态反馈机理,实现了城市安全宜居度变化的居民决策仿真。实例结果证明了多智能体建模方法对于分析城市安全宜居度的有效性,可为城市发展规划提供决策支持。

关键词: 城市宜居度, 城市宜居安全性指标, 复杂城市系统, 多智能体建模, 居民决策

Abstract: Urban livability is an important factor for the decision of modern urban construction. Based on the multi-agent modeling theory, a model for the change of urban livability is proposed, so as to analyze the present situation and changing trend of urban livability in complex urban system. Taking safety as an example, five environmental indicators of public security, fire protection, traffic, emergency response and residence are given. The interactive dynamic feedback mechanism between residents' decision-making behavior and urban safety livability under the influence of the five indicators and policies is provided, and the residents' decision-making simulation of urban safety livability change is implemented. The results show that the multi-agent modeling method is effective in analyzing the urban safety livability and can provide decision support for urban development planning.

Key words: urban livability, urban safety livability index, complex urban system, multi-agent modeling, resident decision

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