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). We use our proposed, and recently published at IEEE BigData 2022 conference, Data-Driven Trajectory Generator, dubbed DDTG, to generate a synthetic vehicle trajectory dataset in the metropolitan city of Los Angeles. The dataset consists of 1.5 million trajectories spanning the first two weeks of December 2019.

Dataset is stored in Parquet format. The most convenient way to process and explore the dataset is using Pandas and the Python programming language but relevant libraries also exist in other programming languages (C++, Java).

All methods are described in the paper "Generation of Synthetic Urban Vehicle Trajectories", IEEE BigData 2022.