Abstract
Cellular signaling data is widely available in mobile communications and contains abundant movement sensing information of individual travelers. Using cellular signaling data to estimate the trajectories of mobile users can benefit many location-based applications, including infectious disease tracing and screening, network flow sensing, traffic scheduling, etc. However, conventional methods rely too much on heuristic hypotheses or hardware-dependent network fingerprinting approaches. To address the above issues, NF-Track (Network-wide Fingerprinting based Tracking) is proposed to realize accurate online map-matching of cellular location sequences. In particular, neither prior assumptions such as arterial preference and less-turn preference or extra hardware-relevant parameters such as RSS and SNR are required for the proposed framework. Therefore, it has a strong generalization ability to be flexibly deployed in the cloud computing environment of telecom operators. In this architecture, a novel segment-granularity fingerprint map is put forward to provide sufficient prior knowledge. Then, a real-time trajectory estimation process is developed for precise positioning and tracking. In our experiments implemented on the urban road network, NF-Track can achieve a recall rate of 91.68% and a precision rate of 90.35% in sophisticated traffic scenes, which are superior to the state-of-the-art model-based unsupervised learning approaches.
Highlights
Mobile phones have become a kind of universal instant messengers nowadays, and their users generate huge amounts of real-time records that form the backbone of ubiquitous computing in heterogeneous sensor networks [1,2,3]
Referring to historical weather information [56], we found that weather conditions have little impact on the performance of NF-Track, because dense base stations are deployed in urban areas, and they serve mobile users well
NF-Track is tested on a real-world urban dataset
Summary
Mobile phones have become a kind of universal instant messengers nowadays, and their users generate huge amounts of real-time records that form the backbone of ubiquitous computing in heterogeneous sensor networks [1,2,3]. As the most well-applied one, GPS positioning data has given vital information for many applications, including traffic sensing, traffic incident detection, travel prediction, route recommendations, etc. The backend systems of GPS service providers could only observe partial location data of users. As a supplement to GPS localization, cellular signaling data has become a promising type of mobile phone data that ceaselessly records the signaling between base stations and every mobile device. It has been widely used in location-based services, including route recommendation, traffic scheduling, etc. A large number of technologies have been proposed for user location sensing based on cellular data [7,8,9,10,11]
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