Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent urban decision making. In this survey, we conduct a systematic literature review on the application of machine learning methods in urban decision making, with a focus on planning, transportation, and healthcare. First, we provide a taxonomy based on typical applications of machine learning methods for urban decision making. We then present background knowledge on these tasks and the machine learning techniques that have been adopted to solve them. Next, we examine the challenges and advantages of applying machine learning in urban decision making, including issues related to urban complexity, urban heterogeneity and computational cost. Afterward and primarily, we elaborate on the existing machine learning methods that aim to solve urban decision making tasks in planning, transportation, and healthcare, highlighting their strengths and limitations. Finally, we discuss open problems and the future directions of applying machine learning to enable intelligent urban decision making, such as developing foundation models and combining reinforcement learning algorithms with human feedback. We hope this survey can help researchers in related fields understand the recent progress made in existing works, and inspire novel applications of machine learning in smart cities.

中文翻译：

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机器学习在城市决策中的应用调查：在规划、交通和医疗保健中的应用


发展智慧城市对于确保可持续发展和改善人类福祉至关重要。构建智慧城市的一个关键方面是设计智能方法来解决城市地区出现的各种决策问题。随着机器学习技术的持续快速发展，越来越多的研究专注于利用这些方法来实现智能城市决策。在这项调查中，我们对机器学习方法在城市决策中的应用进行了系统的文献综述，重点是规划、交通和医疗保健。首先，我们提供了一个基于机器学习方法在城市决策中的典型应用的分类法。然后，我们介绍了有关这些任务的背景知识以及用于解决这些任务的机器学习技术。接下来，我们研究了在城市决策中应用机器学习的挑战和优势，包括与城市复杂性、城市异质性和计算成本相关的问题。之后，我们主要详细阐述了旨在解决规划、交通和医疗保健中城市决策任务的现有机器学习方法，强调了它们的优势和局限性。最后，我们讨论了开放性问题和应用机器学习实现智能城市决策的未来方向，例如开发基础模型以及将强化学习算法与人类反馈相结合。我们希望这项调查可以帮助相关领域的研究人员了解现有工作的最新进展，并激发机器学习在智慧城市中的新应用。