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一种基于历史轨迹的集装箱定位与跟踪地图匹配算法。

A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking.

作者信息

Li Wenfeng, Zhang Wenwen, Gao Cong

机构信息

School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.

出版信息

Sensors (Basel). 2022 Apr 15;22(8):3057. doi: 10.3390/s22083057.

Abstract

Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching algorithms based on the shortest path principle are not applicable for container positioning and tracking. To solve this problem, a historical-trajectories-based map matching algorithm (HTMM) is proposed. HTMM mines the travel time and the frequency in historical trajectories to help find the local path between two adjacent candidate points. HTMM first presents a path reconstruction method to calculate the travel time of historical trajectories on each road segment. A historical path index library based on a path tree is then developed to efficiently index historical paths. In addition, a location query and tracking method is introduced to determine the location of containers at given time. The performance of HTMM is validated on a real freight trajectory dataset. The experimental results show that HTMM has more than 3% and 5% improvement over the ST-Matching algorithm and HMM-based algorithm, respectively, at 60-300 s sampling intervals. The positioning error is reduced by half at a 60 s sampling interval.

摘要

集装箱的定位与跟踪正成为集装箱运输的迫切需求。地图匹配算法已被广泛应用于校正定位误差。由于集装箱轨迹具有采样率低和GPS点缺失的特点,现有的基于最短路径原则的地图匹配算法不适用于集装箱的定位与跟踪。为解决这一问题,提出了一种基于历史轨迹的地图匹配算法(HTMM)。HTMM挖掘历史轨迹中的行程时间和频率,以帮助找到两个相邻候选点之间的局部路径。HTMM首先提出一种路径重建方法,以计算历史轨迹在每个路段上的行程时间。然后开发一个基于路径树的历史路径索引库,以高效地索引历史路径。此外,引入一种位置查询和跟踪方法,以确定给定时间集装箱的位置。在真实货运轨迹数据集上验证了HTMM的性能。实验结果表明,在60 - 300秒的采样间隔下,HTMM分别比ST-Matching算法和基于HMM的算法有超过3%和5%的改进。在60秒的采样间隔下,定位误差减少了一半。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/697dab7f32f9/sensors-22-03057-g001.jpg

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