ATR: Automatic Trajectory Repairing with Movement Tendencies

Abstract

GPS trajectories are always embedded with errors, due to the weather or environmental variables. Existing trajectory repairing methods have employed Kalman filters or sequential data cleaning methods. Kalman filter or its variants change all observed measurements, while generally most measurements are originally accurate. Sequential data cleaning methods are mainly applied on one-dimensional data sequences, and when encountering multi-dimensional trajectories, their performance will be compromised due to that the features of multi-dimensional trajectories are not fully utilized. To address these issues, we propose to repair GPS trajectory with movement tendencies, speed change tendency, travel distance tendency and repair distance tendency. We formalize the tendency based trajectory repairing, and propose an exact solution to find the repair which minimize movement tendency score. Then we propose high quality candidate selection and dynamic error range estimation, to improve the efficiency and effectiveness of exact solution. Experiments on three data sets demonstrate the superiority of our proposal.