About this Research Topic
In recent years, with the rapid development of urban infrastructures and smart devices, huge amounts of big data have been collected. These heterogeneous “urban big data”, including, but not limiting to, vehicle trajectories, traffic volume and speed records, crime and accident reports, and weather/air quality observations, contain rich information about a city and can enable intelligent solutions to various urban challenges, such as traffic management, public safety, environmental protection, and public health. However, it is also very challenging to manage, analyze, and make sense of such big urban data. The recent advances in machine learning and artificial intelligence (AI) techniques provide promising research opportunities for urban big data analytics as well as various application domains.
The goal of this Research Topic is to present state-of-the-art multidisciplinary research across the areas of computer science, civil and environmental engineering, transportation science, operation research, social sciences, health science, and many others on technologies, visionary ideas, case studies, and intelligent systems to manage, analyze and learn from urban big data to address real-world challenges for enabling smart cities and urban intelligence. This Research Topic also aims at identifying future challenges and research directions related to big data techniques and applications to improve urban intelligence.
We invite submissions of high-quality manuscripts reporting research in the area of collecting, processing, managing, mining, analyzing, and understanding various urban big data for urban intelligence applications or scenarios. Topics of interest include, but not limited to:
• Big data collection and processing for urban applications;
• Big data Infrastructures for smart cities;
• Data Mining and machine learning algorithms for urban computing;
• Data-driven decision-making problems in urban settings;
• Urban human behavior analysis and social pattern analysis;
• Urban planning with big data evaluation and assessment;
• Data-driven transportation design and management;
• Big data analytics for urban sustainability and environmental health;
• Big data analytics for public safety problems;
• Big data analytics for COVID handling or other public health problems in urban areas.
Topic Editor Jie Bao is an employee of JD Digits, China.
The goal of this Research Topic is to present state-of-the-art multidisciplinary research across the areas of computer science, civil and environmental engineering, transportation science, operation research, social sciences, health science, and many others on technologies, visionary ideas, case studies, and intelligent systems to manage, analyze and learn from urban big data to address real-world challenges for enabling smart cities and urban intelligence. This Research Topic also aims at identifying future challenges and research directions related to big data techniques and applications to improve urban intelligence.
We invite submissions of high-quality manuscripts reporting research in the area of collecting, processing, managing, mining, analyzing, and understanding various urban big data for urban intelligence applications or scenarios. Topics of interest include, but not limited to:
• Big data collection and processing for urban applications;
• Big data Infrastructures for smart cities;
• Data Mining and machine learning algorithms for urban computing;
• Data-driven decision-making problems in urban settings;
• Urban human behavior analysis and social pattern analysis;
• Urban planning with big data evaluation and assessment;
• Data-driven transportation design and management;
• Big data analytics for urban sustainability and environmental health;
• Big data analytics for public safety problems;
• Big data analytics for COVID handling or other public health problems in urban areas.
Topic Editor Jie Bao is an employee of JD Digits, China.
Keywords: Urban Intelligence, Smart Cities, Spatio-Temporal Data, Machine Learning, Big Data Analytics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
