Abstract
Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things, for which situational awareness is normally the crucial first step. While traditional static surveillance data on cities have been available for decades, this review reports a type of relatively new yet highly important urban data source, i.e., the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city. We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System (GPS). This technique enjoys advantages such as a large penetration rate (∼50% urban population covered), uniform spatiotemporal coverage, and high localization precision. We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced. Then we introduce two suites of empowering technologies that help fulfill the requirements of (1) cybersecurity insurance for smart cities and (2) spatiotemporal modeling and visualization for situational awareness, both via big mobile data. The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.
摘要
智慧城市态势感知近年来成为学术圈、 产业界及政府部门关注的热门话题. 其整合尖端信息技术的潜力可望解决现代城市面临的诸多挑战. 在最近一期五年规划中, 中国政府强调利用前沿信息技术(如大数据、物联网)赋能智慧城市管理, 其中态势感知通常是关键的第一步. 近年来, 面向城市态势的静态监测数据已广泛存在. 与之不同的是, 本文报告了一类相对新颖且极为重要的新兴城市数据源, 即在移动设备上收集的大规模移动数据, 可代表现代城市中公共车辆和个人用户的移动情况与分布. 具体而言, 我们重点关注一种代表性数据源, 整合了数十万移动软件应用程序中获取的百亿条GPS定位数据, 服务于智慧城市态势感知. 这种数据源具有较高的用户渗透率(覆盖约50%的城市人口)、 均匀的时空覆盖程度和高定位精度等优势. 本文首先详述了智慧城市态势感知的需求与挑战, 之后重点介绍了两类面向态势感知的移动大数据分析技术: (1)智慧城市的安全保障方法; (2)智慧城市移动大数据的时空建模与可视化分析方法. 本文主要贡献在于全面阐述智慧城市态势感知的技术框架, 并通过实际应用案例展示其技术可行性.
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Zhiguang SHAN, Lei SHI, Bo LI, Yanqiang ZHANG, and Xiatian ZHANG drafted the paper. Wei CHEN helped organize the paper. Lei SHI, Bo LI, Yanqiang ZHANG, and Xiatian ZHANG revised and finalized the paper.
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Zhiguang SHAN, Lei SHI, Bo LI, Yanqiang ZHANG, Xiatian ZHANG, and Wei CHEN declare that they have no conflict of interest.
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Project supported by the National Key R&D Program of China (No. 2021YFB3500700), the National Natural Science Foundation of China (No. 62172026), the National Social Science Fund of China (No. 22&ZD153), the Key R&D “Jianbin” Tackling Plan Program in Zhejiang Province, China (No. 2023C01119), the Fundamental Research Funds for the Central Universities, China, and the State Key Laboratory of Software Development Environment, China
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Shan, Z., Shi, L., Li, B. et al. Empowering smart city situational awareness via big mobile data. Front Inform Technol Electron Eng 25, 286–307 (2024). https://doi.org/10.1631/FITEE.2300453
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DOI: https://doi.org/10.1631/FITEE.2300453