Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (6): 14-27.doi: 10.3969/j.issn.1000-5641.2023.06.002

• Computer Science • Previous Articles     Next Articles

Method for improving the quality of trajectory data for riding-map inference

Jie CHEN, Wenyi SHEN, Wenyu WU, Jiali MAO*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2022-07-06 Online:2023-11-25 Published:2023-11-23
  • Contact: Jiali MAO


The trajectory optimization of cycling is hindered by the errors of positioning equipment, riding habits of non-motor vehicles, and other factors. It leads to quality problems, such as abnormal data and missing positioning information in the riding trajectory, impacting the application of trajectory-based riding-map inference and riding-path planning. To solve these problems, this paper creates a framework for improving the quality of cycling-trajectory data, based on the construction of a grid index, screening of abnormal trajectory points, elimination of wandering trajectory segments, elimination of illegal trajectory segments, calibration of drift trajectory segments, and recovery of missing trajectory. Comparative and ablation experiments are conducted by using a real non-motor-vehicle cycling-trajectory dataset. The experimental results verify that the proposed method improves the accuracy of cycling-map inference.

Key words: wandering trajectory, drift trajectory, trajectory recovery

CLC Number: