Generative adversarial deep learning model for producing location-based synthetic trajectory data

The rapid expansion of location-based services has triggered the acquisition and analysis of various types of individual trajectory recordings. However, the sensitive nature of such kind of data inevitably leads to privacy constraints and regulations on its use and sharing. This paper addresses the problem in a distinctive perspective. Instead of blurring or modifying original trajectory samples, we aim to generate a completely synthetic dataset, whose samples are singularly different from the original ones, but whose collective sets share similar global characteristics and performances. We propose a generative deep learning solution for location-based trajectory formats, with the goal of producing realistic synthetic location sequences: the process relies on a generative adversarial network (GAN) framework, involving long short-term memory (LSTM) recurrent layers to capture trajectory characteristics, and neural embeddings to model mobility relations between places. We leverage multiple metrics to assess the realistic character of synthetic data and their similarity with the original source; moreover, we evaluate downstream performance differences with regard to the next place prediction problem. Tested on a real-world large-scale dataset of long-distance trips, and compared with baselines and traditional geomasking techniques, our approach presents better characteristics, providing novel insights into GeoAI solutions for human mobility analysis.