Outdoor Position Recovery from HeterogeneousTelco Cellular Data

TL;DR

This paper introduces PRNet+, a multi-task deep learning framework that improves outdoor position recovery from heterogeneous cellular data by simultaneously detecting transportation modes and learning complex spatio-temporal patterns.

Contribution

It presents a novel multi-task deep neural network that effectively handles heterogeneous mobility patterns in cellular data, outperforming existing methods.

Findings

PRNet+ significantly reduces localization errors.

The framework accurately detects transportation modes.

It outperforms state-of-the-art approaches on multiple datasets.

Abstract

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.