Outdoor Position Recovery From Heterogeneous Telco Cellular Data

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. To this end, we propose a multi-task learning-based deep neural network (DNN) framework, namely
${{\sf PRNet}}$
$^+$
, to incorporate outdoor position recovery and transportation mode detection. To make sure that
${{\sf PRNet}}$
$^+$
can work, we develop a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that
${{\sf PRNet}}$
${^{+}}$
greatly outperforms state-of-the-arts by lower localization errors.