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Understanding Human Activity and Urban Mobility Patterns From Massive Cellphone Data: Platform Design and Applications
IEEE Intelligent Transportation Systems Magazine ( IF 3.6 ) Pub Date : 2020-04-02 , DOI: 10.1109/mits.2019.2962146 Zhenxing Yao , Yu Zhong , Qiang Liao , Jie Wu , Haode Liu , Fei Yang
IEEE Intelligent Transportation Systems Magazine ( IF 3.6 ) Pub Date : 2020-04-02 , DOI: 10.1109/mits.2019.2962146 Zhenxing Yao , Yu Zhong , Qiang Liao , Jie Wu , Haode Liu , Fei Yang
Large-scale cellphone data provide an emerging source for acquiring urban movements and patterns. Existing researches on cellphone data based travel behavior detection are mostly concentrated on algorithms exploration and evaluation. The framework and layers of the monitoring platform from data collection to application are not very clear, and existing platform are mostly concentrated on limited functions, some important travel characteristics and influencing factors have not been clearly explored. Therefore, this paper tries to propose a real-time urban mobility monitoring and traffic management platform using cellular data. First, the systems and essential modules of the platform including global system architecture, functional service system, core technology system, data system, and security & privacy protection system are proposed. Second, a field case study in Guiyang China, covering over 3 million people daily and a total 3 years 600TB cellphone data, is carried out to evaluate the methods. 6 visualization platforms 36 sub-functions including jobs-housing distribution, real-time passenger mobility, regional contacting, hotspot passenger attraction, traffic congestion and distribution etc. are constructed and analyzed. This paper aims to provide practical experience and reference for urban mobility monitoring and traffic management system construction using cellular data.
更新日期：2020-04-02
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