  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. we propose a multi-task
learning-based deep neural network (DNN) framework, namely PRNet+, to
incorporate outdoor position recovery and transportation mode detection. To
make sure the framework work, PRNet+ develops 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 PRNet+ greatly
outperforms state-of-the-arts.
