Generation of Synthetic Urban Vehicle Trajectories

Abstract

The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. And even when they are, they are limited in size and spatio-temporal coverage. To address these issues a number of methods for generating synthetic yet realistic trajectory datasets have been proposed. These existing methods either require a lot of complex parameters to be calibrated (simulators) or rely on existing trajectory datasets (generative models). In this paper, we propose Data-Driven Trajectory Generator, dubbed DDTG, a data-driven, model-free, and parameter-less algorithm for generating realistic synthetic vehicle trajectory datasets. Unlike existing approaches, DDTG relies on aggregate origin-destination and traffic data, both of which are publicly available and free of privacy concerns. Furthermore, we show that our method is orthogonal to the existing approaches with which DDTG can be combined to generate synthetic datasets of higher quality. Our experiments with real-world trajectory and traffic data show that the datasets generated by DDTG follow distributions that are very close to the distributions of real trajectory datasets.