A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare

Abstract

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 at solving 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.