Abstract:

With the acceleration of urbanization and digitalization, the importance of urban resource allocation, commercial facility layout, and emergency management has become increasingly prominent. While traditional methods have achieved positive results in static scenarios, they reveal significant limitations when dealing with high-dimensional and dynamic geospatial data. In recent years, artificial intelligence technologies, particularly deep reinforcement learning (DRL) methods, have offered novel approaches to optimizing urban facility allocation. By continuously learning through interaction with its environment, DRL can handle complex sequential decision-making problems. Supported by geographic big data, it demonstrates strong adaptability and intelligent advantages, effectively addressing the shortcomings of traditional methods. However, its application still faces challenges such as high model training costs and strong dependence on data quality. Future research should focus on optimizing DRL algorithm structures, enhancing model training efficiency, strengthening generalization capabilities across diverse scenarios, and exploring the integration of DRL with other intelligent optimization methods. This will further expand the depth and breadth of its application in urban facility allocation optimization.

Key words: optimization of urban facility allocation, spatio-temporal big data, urban facility siting, artificial intelligence, deep reinforcement learning