CDR-Based Trajectory Reconstruction Using Transformers

Abstract

With the development of telecommunication technologies, mobile devices, and data collected via mobile services, it has become of great interest to predict the paths that individuals take in cities. With sparse mobility data, the goal of researchers is to build models that are able to fill the gaps, or in other words, to reconstruct the trajectory of an individual. Recent models proposed for this task utilize Call Detail Records (CDRs) produced when a mobile phone connects to the cellular network, using Monte Carlo or Hidden Markov Model (HMM) based approaches. In this thesis, a novel deep learning method for trajectory reconstruction from CDR data is introduced. GPS points are linked to roads on a road network constructed from the OpenStreetMap (OSM) database, and the resulting labels are used in training as ground truth. Drawing inspiration from prior work in matching GPS points to a network of roads using Transformer neural networks, we present a framework that involves using two Transformers sequentially with partially modified architectures. The final result is a trained Transformer, able to predict the road level path, knowing only the cell, in the area in which movement started. The accuracy of estimating the taken path was compared with that of prior approaches which use probabilistic modeling to predict the next location from CDR data.