Synthetic vehicle trajectory dataset for the metropolitan city of Los Angeles using DDTG - Anastasiou, Chrysovalantis; Kim, Seon Ho; Shahabi, Cyrus (Dryad, 2022)
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.
All methods are described in the paper "Generation of Synthetic Urban
Vehicle Trajectories", IEEE BigData 2022.
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).
Keywords: FOS: Computer and information sciences; synthetic vehicle trajectories; trajectory generator; vehicle trajectories; mobility