VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and Visualization Platform Using Deep Reinforcement Learning

Jinmeng Rao, Song Gao, Xiaojin Zhu

Abstract: Trajectory data is among the most sensitive data and the society increasingly raises privacy concerns. In this demo paper, we present a privacy-preserving Vehicle Trajectory Simulation and Visualization (VTSV) web platform (demo video: https://youtu.be/NY5L4bu2kTU), which automatically generates navigation routes between given pairs of origins and destinations and employs a deep reinforcement learning model to simulate vehicle trajectories with customized driving behaviors such as normal driving, overspeed, aggressive acceleration, and aggressive turning. The simulated vehicle trajectory data contain high-sample-rate of attributes including GPS location, speed, acceleration, and steering angle, and such data are visualized in VTSV using streetscape.gl, an autonomous driving data visualization framework. Location privacy protection methods such as origin-destination geomasking and trajectory k-anonymity are integrated into the platform to support privacy-preserving trajectory data generation and publication. We design two application scenarios to demonstrate how VTSV performs location privacy protection and customize driving behavior, respectively. The demonstration shows that VTSV is able to mitigate data privacy, sparsity, and imbalance sampling issues, which offers new insights into driving trajectory simulation and GeoAI-powered privacy-preserving data publication.