面向骑行地图推断的轨迹数据质量提升方法

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

由于定位设备误差、非机动车骑行习惯等因素的影响,骑行轨迹存在数据异常与定位信息缺失等质量问题,为骑行地图推断和骑行路径规划等基于轨迹的应用带来了极大挑战.为解决上述问题,提出了一个面向骑行地图推断的轨迹数据质量提升框架,包括网格索引构建、异常轨迹点的消除、徘徊轨迹段的消除、违章轨迹段的消除、漂移轨迹段的校准以及缺失轨迹的恢复等.在真实非机动车骑行轨迹数据集上进行了对比实验和消融实验,实验结果验证了所提方案对于提升骑行地图推断的精度优于现有方法.

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.