A privacy-preserving vehicle trajectory clustering framework

As one of the essential tools for spatio–temporal traffic data mining, vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles. However, uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage. Therefore, one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy. We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model (IKV) based on the variational autoencoder (VAE) and an improved K-means algorithm. In the framework, the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server; the server uses the hidden variables for clustering analysis and delivers the analysis results to the client. The IKV’ workflow is as follows: first, we train the VAE with historical vehicle trajectory data (when VAE’s decoder can approximate the original data, the encoder is deployed to the edge computing device); second, the edge device transmits the hidden variables to the server; finally, clustering is performed using improved K-means, which prevents the leakage of the vehicle trajectory. IKV is compared to numerous clustering methods on three datasets. In the nine performance comparison experiments, IKV achieves optimal or sub-optimal performance in six of the experiments. Furthermore, in the nine sensitivity analysis experiments, IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations. These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments, such as carpooling tasks, but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.